Climatic Series Research Articles

Revisiting historical climatic signals to better explore the future: prospects of water cycle changes in Central Sahel

Abstract. Rainfall and climatic conditions are the main drivers of natural and cultivated vegetation productivity in the semiarid region of Central Sahel. In a context of decreasing cultivable area per capita, understanding and predicting changes in the water cycle are crucial. Yet, it remains challenging to project future climatic conditions in West Africa since there is no consensus on the sign of future precipitation changes in simulations coming from climate models. The Sahel region has experienced severe climatic changes in the past 60 years that can provide a first basis to understand the response of the water cycle to non-stationary conditions in this part of the world. The objective of this study was to better understand the response of the water cycle to highly variable climatic regimes in Central Sahel using historical climate records and the coupling of a land surface energy and water model with a vegetation model that, when combined, simulated the Sahelian water, energy and vegetation cycles. To do so, we relied on a reconstructed long-term climate series in Niamey, Republic of Niger, in which three precipitation regimes can be distinguished with a relative deficit exceeding 25% for the driest period compared to the wettest period. Two temperature scenarios (+2 and +4 °C) consistent with future warming scenarios were superimposed to this climatic signal to generate six virtual future 20-year climate time series. Simulations by the two coupled models forced by these virtual scenarios showed a strong response of the water budget and its components to temperature and precipitation changes, including decreases in transpiration, runoff and drainage for all scenarios but those with highest precipitation. Such climatic changes also strongly impacted soil temperature and moisture. This study illustrates the potential of using the strong climatic variations recorded in the past decades to better understand potential future climate variations.

AS INCERTEZAS ASSOCIADAS ÀS CONDIÇÕES CLIMÁTICAS OBTIDAS PELO MODELO ETA CPTEC/HADCM3: AVALIAÇÃO COMPARATIVA ENTRE OS DADOS SIMULADOS E OBSERVADOS DE PRECIPITAÇÃO, EVAPOTRANSPIRAÇÃO E VAZÃO NA BACIA HIDROGRÁFICA DO RIO IJUÍ, BRASIL.

Este estudo tem como objetivo avaliar as condições climáticas obtidas pelo modelo Eta CPTEC/HadCM3, conduzido por quatro membros do modelo global HadCM3 (CNTRL, LOW, MID e HIGH), na bacia do rio Ijuí, RS. O período de controle utilizado foi de 1961 a 1975, visando a avaliação dos cenários climáticos e de vazão entre 1976 e 1990. O trabalho foi estruturado em cinco etapas: interpolação espacial dos dados climáticos; correção das séries climáticas simuladas; cálculo da evapotranspiração de referência; simulação hidrológica da vazão mensal; comparação entre as condições simuladas e as observadas de precipitação, evapotranspiração e vazão. Embora os métodos de correção utilizados para eliminar as tendências das séries climáticas simuladas tenham originado cenários bem distintos, nenhum método apresentou desempenho superior a outro em todos os critérios analisados. Tanto no caso das precipitações, quanto das vazões resultantes do processo de modelagem hidrológica, as diferenças entre os valores simulados com base no modelo Eta e os valores observados atingiram erros algumas vezes superiores a 20%. Portanto, deve-se considerar que estas incertezas serão reproduzidas em cenários futuros, quanto à análise dos efeitos das mudanças climáticas na disponibilidade hídrica.

Sensitivity of lake thermal and mixing dynamics to climate change

Warming-induced changes in lake thermal and mixing regimes present risks to water quality and ecosystem services provided by U.S. lakes and reservoirs. Modulation of responses by different physical and hydroclimatic settings are not well understood. We explore the potential effects of climate change on 27 lake “archetypes” representative of a range of lakes and reservoirs occurring throughout the U.S. Archetypes are based on different combinations of depth, surface area, and water clarity. LISSS, a one-dimensional dynamic thermal simulation model, is applied to assess lake response to multiple mid-21st century change scenarios applied to nine baseline climate series from different hydroclimatic regions of the U.S. Results show surface water temperature increases of about 77 % of increase in average air temperature change. Bottom temperature changes are less (around 30 %) for deep lakes and in regions that maintain mid-winter air temperatures below freezing. Significant decreases in length of ice cover are projected, and the extent and strength of stratification will increase throughout the U.S., with systematic differences associated with depth, surface area, and clarity. These projected responses suggest a range of future challenges that lake managers are likely to face. Changes in thermal and mixing dynamics suggest increased risk of summer hypoxic conditions and cyanobacterial blooms. Increased water temperatures above the summer thermocline could be a problem for cold water fisheries management in many lakes. Climate-induced changes in water balance and mass inputs of nutrients may further exacerbate the vulnerability of lakes to climate change.

Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data

In this paper, the discrete wavelet transform (DWT), the Mann–Kendall (MK) trend test, and the sequential Mann–Kendall test are applied to temperature series at different time scales in order to detect the long-term trends (1956–2010) in synoptic-scale surface temperatures in Iran, as well as the dominant time scales affecting these temperature time series. The relevant data was extracted from a gridded data file of the region (44°E to 63.5°E, 25°N to 40°N) and divided into 12 regular zones (of dimensions 5×5°), each of which was analyzed as an individual unit. The results of this research show that at the monthly, seasonal and annual time scales, the trends in temperature were significant and positive in all of the study zones. In addition, the 2-month and 4-month components were dominant at the monthly time scale, the 48-month component dominant at the seasonal time scale, and the 8-year and 16-year components dominant at the annual time scale. Also, the temperature trends in the northern, and especially central, regions of the study zones increased from west to east, and these increasing trends in temperature were most prominent for the spring and summer seasons. The methodology applied here is generally applicable and quite useful for studying both trends and the dominant time scales affecting climatic data series and could find significant applications in related fields.

Distribution of limited irrigation water based on optimized regulated deficit irrigation and typical metheorological year concepts

Under limited availability of irrigation water during the growing season, farmers must avoid a premature depletion of this resource that may lead to a sizeable decrease in yield. Four levels of net irrigation availability were considered for an onion (Allium cepa L.) crop cultivated in Castilla-La Mancha region (Spain): 1.0 INref (typical net irrigation requirements in the area: 743.3mm); 0.9 INref; 0.8 INref; and 0.7 INref. The available amount of irrigation water was distributed during the growing period by combining the modified optimized regulated deficit irrigation (ORDI) [which determines the actual evapotranspiration (ETa) and maximum evapotranspiration (ETm) ratios (ETa/ETm) per growing stage that maximize yield for a certain amount of irrigation water] and the typical meteorological year (TMY) methodologies. So, optimized irrigation schedules that reach the optimized ETa/ETm ratios per stage and yields were calculated for three TMYs (dry, intermediate, and wet) obtained from a 54-year climatic series (1951–2004). The six months before sowing of each year were analysed to determine if that period was dry, intermediate, or wet. The yield obtained for each level of irrigation availability and the corresponding TMY was considered as the forecasted yield for the current year. Optimized irrigation schedules were corrected during the growing period by replacing the daily climatic data of the TMY by those of the current year. At the end of each growing stage, optimized irrigation schedules were recalculated taking into account the real amount of irrigation water available for the following periods and the forecasted climatic conditions. The effect of this methodology on final yield was simulated by MOPECO (economic optimization model for irrigation water management) during 8 seasons (2005–2012). As a result, onion yields at harvest were approximately 5.1% lower than the forecasted yield at the beginning of the growing period for the different availabilities of irrigation water. As a consequence of premature depletion of irrigation water, only 3 of 24 analysed scenarios reached excessive deficit during the last stage.

A Reanalysis of Long-Term Surface Air Temperature Trends in New Zealand

Detecting trends in climate is important in assessments of global change based on regional long-term data. Equally important is the reliability of the results that are widely used as a major input for a large number of societal design and planning purposes. New Zealand provides a rare long temperature time series in the Southern Hemisphere, and it is one of the longest continuous climate series available in the Southern Hemisphere Pacific. It is therefore important that this temperature dataset meets the highest quality control standards. New Zealand’s national record for the period 1909 to 2009 is analysed and the data homogenized. Current New Zealand century-long climatology based on 1981 methods produces a trend of 0.91 °C per century. Our analysis, which uses updated measurement techniques and corrects for shelter-contaminated data, produces a trend of 0.28 °C per century.

Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980–2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

THE IMPORTANCE OF THE QUALITY AND RELIABILITY OF THE HISTORICAL TIME SERIES FOR THE STUDY OF CLIMATE CHANGE

The value of the datasets employed in climate changes analysis strongly depends on the homogeneity of the time series. In fact, once climate change became an issue of central importance, there arose a much needed skepticism about the results of data analysis work, which frequently indicated sharp or determined changes in regional climates. It is now well recognized that variations in many long term station time series are caused not only by changes in weather and climate, but also by changes in location of the stations, instruments, formulae used to calculate means, observing practices and station environment (Karl and Williams, 1987; Heino, 1994; Gokurk et al ., 2008). A homogeneous climate time series is defined as one where variations are caused only by variations in weather and climate (Conrad and Pollak, 1950; Aguilar et al., 2003). Other, unnatural variations are called inhomogenities, which can lead to erroneous interpretations of the studied climate. Thus, before using climatological data in any kind of study – especially in climate change studies – a quality control and homogeneity assessment of data set is mandatory (Peterson et al., 1998). Long instrumental homogeneous records are a basic requirement for evaluating the smooth running of the variables over time and compare the measurement stations and the geographical areas to which they belong. Just to pursue this important objective, a few years ago, between 2007–2012, the European COST Action ES0601 called HOME (Advances in homogenisation methods of climate series) project was devoted to evaluate the performance of homogenization methods used in climatology and produce a software that would be a synthesis of the best aspects of some of the most efficient methods.

Homogenization of climate series: The basis for assessing climate changes

Long-term meteorological observation series are fundamental for reflecting climate changes. However, almost all meteorological stations inevitably undergo relocation or changes in observation instruments, rules, and methods, which can result in systematic biases in the observation series for corresponding periods. Homogenization is a technique for adjusting these biases in order to assess the true trends in the time series. In recent years, homogenization has shifted its focus from the adjustments to climate mean status to the adjustments to information about climate extremes or extreme weather. Using case analyses of ideal and actual climate series, here we demonstrate the basic idea of homogenization, introduce new understanding obtained from recent studies of homogenization of climate series in China, and raise issues for further studies in this field, especially with regards to climate extremes, uncertainty of the statistical adjustments, and biased physical relationships among different climate variables due to adjustments in single variable series.

Updated Homogenized Chinese Temperature Series with Physical Consistency

AbstractMost methods of homogenization of climate data are applied to time series of a single variable, such as daily maximum temperature (Tmax) or daily minimum temperature (Tmin). Consequently, the physical relationship among different variables, e.g., Tmax>Tmin, may be distorted after homogenization of climate series of individual variables. The authors develop a solution to improve consistency among diurnal temperature records, while using the Multiple Analysis of Series for Homogenization (MASH) method to homogenize the observation series of daily mean temperature (Tm), Tmin, and Tmax at 545 stations in China for the period 1960–2011, called CHTM2.0. In the previous version of this homogenized dataset based on MASH (CHTM1.0) for the period 1960–2008, there are a few records (0.039% of the total) that are physically inconsistent. For developing CHTM2.0, the authors apply additional adjustments for each day with inconsistent temperature records, in order to hold Tmax>Tm>Tmin. Although the additional ad...

Hydrological simulation using SWAT model in headwater basin in Southeast Brazil

Hydrological models are important tools that have been used in water resource planning and management. Thus, the aim of this work was to calibrate and validate in a daily time scale, the SWAT model (Soil and Water Assessment Tool) to the watershed of the Galo creek , located in Espírito Santo State. To conduct the study we used georeferenced maps of relief, soil type and use, in addition to historical daily time series of basin climate and flow. In modeling were used time series corresponding to the periods Jan 1, 1995 to Dec 31, 2000 and Jan 1, 2001 to Dec 20, 2003 for calibration and validation, respectively. Model performance evaluation was done using the Nash-Sutcliffe coefficient (E NS) and the percentage of bias (P BIAS). SWAT evaluation was also done in the simulation of the following hydrological variables: maximum and minimum annual daily flowsand minimum reference flows, Q90 and Q95, based on mean absolute error. E NS and P BIAS were, respectively, 0.65 and 7.2% and 0.70 and 14.1%, for calibration and validation, indicating a satisfactory performance for the model. SWAT adequately simulated minimum annual daily flow and the reference flows, Q90 and Q95; it was not suitable in the simulation of maximum annual daily flows.

Virtual experiments using a participatory model to explore interactions between climatic variability and management decisions in extensive grazing systems in the basaltic region of Uruguay

Agricultural production in “open-sky systems” such as extensive cattle ranching on natural grasslands is subject to inter-annual climatic variability and other market fluctuations. To tackle the dependency of livestock breeding on these factors, we conducted participatory modeling with cattle producers in Uruguay. The methodology consists of simulating possible scenarios to collectively evaluate the different herd management practice alternatives. In this paper, we present an Agent-Based Model built with stakeholders and designed to represent a breeding system on a typical extensive grazing area in the basaltic soils region (BR) of Uruguay. This model has three main modules: environment, biophysical and decisional sub-models. This modularity allows the conducting of virtual experiments to reveal how some herd management decisions (such as seasonal stocking rate adjustments) combined with a climatic series can result in resilience against drought periods and market movements. Long-term simulations were implemented to analyze the sensitivity of the model to key management parameters with varying climate conditions. The inter-annual climatic variability can seriously affect cattle production, even with conservative stocking rates. Rigid strategies are bound to fail and cause systems to break. Adaptive management emerged as a critical option for the sustainability of livestock breeding. The inter-annual climatic variability can seriously affect cattle production, even with conservative stocking rates. This result highlights the importance of adaptive management, one that can react to a changing environment, for the sustainability of livestock breeding.

HAC robust trend comparisons among climate series with possible level shifts

Comparisons of trends across climatic data sets are complicated by the presence of serial correlation and possible step‐changes in the mean. We build on heteroskedasticity and autocorrelation robust methods, specifically the Vogelsang–Franses (VF) nonparametric testing approach, to allow for a step‐change in the mean (level shift) at a known or unknown date. The VF method provides a powerful multivariate trend estimator robust to unknown serial correlation up to but not including unit roots. We show that the critical values change when the level shift occurs at a known or unknown date. We derive an asymptotic approximation that can be used to simulate critical values, and we outline a simple bootstrap procedure that generates valid critical values and p‐values. Our application builds on the literature comparing simulated and observed trends in the tropical lower troposphere and mid‐troposphere since 1958. The method identifies a shift in observations around 1977, coinciding with the Pacific Climate Shift. Allowing for a level shift causes apparently significant observed trends to become statistically insignificant. Model overestimation of warming is significant whether or not we account for a level shift, although null rejections are much stronger when the level shift is included. © 2014 The Authors. Environmetrics published by John Wiley & Sons, Ltd.

A simple model to predict phenology in malting barley based on cultivar thermo-photoperiodic response

In the Pampean region of Argentina, farming systems are based on intensive land use, wheat/soybean double cropping being a key component of these agricultural systems. However, during the last years farmers have been replacing wheat for barley due to its earlier maturity date, reducing yield penalization in soybean as a consequence of delays in its sowing date, and improving the economic profits of the double cropping system. To maximize the benefits of barley/soybean double cropping system, the proper timing of key barley ontogenic stages should be easily identifiable by farmers. The objectives of the present study were to (i) characterize crop phenology through thermo-photoperiod models in response to different sowing dates, (ii) use the algorithms calculated in point (i) to generate a simple model for predicting phenology, using historical climatic series, and (iii) validate the model with independent data. Barley cultivars used in the present study did not show vernalization requirements. Variations in phenology, measured in thermal time, were mainly associated with variations in photoperiod sensitivity during the emergence-heading phase. Significant differences in photoperiodic sensitivity, from −65°Cdh−1 (Scarlett) to −344°Cdh−1 (Q. Ayelen), were observed among cultivars. However, cultivars did not show significant differences in critical photoperiod or intrinsic earliness. The slope of the relationship between heading time and date of emergence, calculated from the algorithms used to build the model, based on thermo-photoperiodic response, varied between locations and cultivars. When the model was tested with an independent data set, predictions for the sowing-flowering phase showed a root mean square error lower than 4% (similar to that observed using more complex models). The algorithms used in the model were masked into a friendly frame and outputs were shown in a simple and attractive manner for users. The model was uploaded to the web site of the University of Buenos Aires to be used by students, advisors, professionals and farmers barley.

SueMulador: Herramienta para la Simulación de Datos Faltantes en Series Climáticas Diarias de Zonas Ecuatoriales

En la actualidad, los modelos de cultivo son una herramienta útil a la hora de tomar decisiones, no obstante, pese a su disponibilidad y facilidad de uso, la información necesaria para utilizarlos no existe o no tiene la calidad suficiente. Un ejemplo preciso del déficit de calidad en los datos lo constituyen las series meteorológicas diarias en las que predominan faltantes. Para hacer frente a esta situación y poder utilizar los modelos de cultivo se elaboró un software para llenar espacios vacíos en series climáticas diarias, basada en una cadena de Markov de orden dos y dos estados. La herramienta llamada SueMulador, se probó y validó con éxito en tres estaciones ubicadas en zonas contrastantes de la geografía colombiana.

Increasing resolution of climate assessment and projection of temperature and precipitation in an alpine area

In mountain regions, important differences in the time trends of climate series can be detected even within relatively small areas, leading to uncertainty when assessing climate change. The paper deals with a structured algorithm for high-resolution downscaling of climate characterisation in a region (precipitation and temperature), leading to a twofold application: increasing spatial resolution of past climate definition for the area and attaining high-resolution downscaling for climate projections. In the first stage, multi-variate analysis (‘partial least squares’ regression) was applied to a number of time series (10) in order to obtain climate averages for a larger number of sites. Predictions made with single-site values (such as seasonal means) can in some cases be improved by applying ‘random perturbation’ of the value and averaging single predictions in the ensemble. This analysis laid the foundation for implementing the same technique to the output of statistical downscaling of multi-model climate projections. Climate shift in the study area (Trentino), located in the north-eastern Italian Alps, was simulated for two 30-year time windows: 2021–2050 and 2071–2099. Progressive warming is predicted, being stronger in the summer, along with a mixed, seasonally differentiated trend for precipitation.

Oak decline analyzed using intraannual radial growth indices, δ(13)C series and climate data from a rural hemiboreal landscape in southwesternmost Finland.

Decline of pedunculate oak (Quercus robur L.) was studied in SW Finland. This is a region where the species is growing near its northern distributional limit globally and a recent decline of mature trees has been described regionally. Tree rings of declining oaks were compared to the chronologies of healthy and oaks that died, climate series and stable isotope discrimination of carbon (δ(13)C) of comparable mature trees. The radial growth (earlywood, latewood, and annual ring width) of declining oaks was clearly deteriorated in comparison to healthy oaks, but recuperated, compared to oaks that died, through all index types. Comparison of climate relationships between growth and δ(13)C, expected to reflect oaks' intrinsic water use efficiency, indicated enhancing resistance to droughts through the growing season. The growth and the climatic growth response was differentiated in declining oaks as compared with the healthy and oaks that died revealing that: (1) declining oaks exhibited decreasing competitive strength as indicated by reduced overall growth relative to healthy oaks, (2) the growth of declining oaks was more sensitive to winter conditions, but less restricted by summer droughts than the growth of other oaks, and (3) healthy oaks were seen having benefitted from the ongoing lengthening of the growing season. Lack of correlativity between growth and δ(13)C became evident as their responses to temperature and precipitation variations deviated drastically during the other but summer months. Our results indicate that several different ecological factors, rather than a single climatic factor (e.g., drought), are controlling the oak decline in the studied environment.

Hydrological reconstruction from tree-ring multi-proxies over the last two centuries at the Caniapiscau Reservoir, northern Québec, Canada

The objective of this study was to reconstruct 200 years of spring (Qspr), summer (Qsum) and annual (Qann) water supply variability at the Caniapiscau Reservoir in a remote area of northern Quebec. This region which is a key hydropower region of North America lacks long-term hydrological series, and tree-ring proxies are seen here as the best alternative for the extension of the climatic series beyond instrumental records. Thus, ring widths, ring densities and stable isotope ratios (δ13C and δ18O) were used to perform paleohydrological reconstructions. The following reconstruction techniques were evaluated for each variable reconstructed: partial least square (PLS) regression applied to all of the tree-ring series, PLS regression applied to selected tree-ring series, and the best analogue method (BAM) applied to selected tree-ring series. These three reconstructions were then combined in a composite reconstruction.

Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe

This paper presents a procedure in which the water-driven water productivity model AquaCrop was fine-tuned and validated for maize for the local conditions in Zimbabwe and then applied to develop sowing management options for decision support. Data from experiments of 2 seasons in Harare and from 5 other sites around Zimbabwe were used for the local calibration and validation of AquaCrop. Model parameters such as the reference harvest index (HIo); the canopy growth coefficient (CGC); early canopy decline and normalised biomass water productivity (WPb*) were adjusted during model calibration. Model performance was satisfactory after calibration with a Nash-Sutcliffe model efficiency parameter (EF = 0.81), RMSE = 15% and R2 = 0.86 upon validation. To develop sowing guidelines, historical climate series from 13 meteorological stations around Zimbabwe were used to simulate maize yield for 6 consecutive sowing dates determined according to criteria applicable in Zimbabwe. Three varieties and typical shallow and deep soil types were considered in the simulation scenarios. The simulated yield was analysed by an optimisation procedure to select the optimum sowing time that maximised long-term mean yield. Results showed that highest yields depended on the climate of the site (rainfall availability), variety (length of growing cycle) and soil depth (soil water storage capacity). The late variety gave higher mean yields for all sowing dates in the maize belt. Staggered sowing is recommended as a way of combating the effects of rainfall variability and as an answer to labour constraints.Keywords: biomass water productivity, AquaCrop, maize sowing dates, crop modelling

Carbon and oxygen isotopes of lakeshore black spruce trees in northeastern Canada as proxies for climatic reconstruction

In the boreal zone of northeastern Canada, paleoclimatic reconstructions of millennial length are rare and long isotopic climatic records are unavailable. However, millennial tree-ring series could be constructed within the region by cross-dating sub-fossil stems preserved in the littoral part of lakes. Thus, there is a need to evaluate the potential of using stable isotopes of lakeshore black spruce trees (Picea mariana [Mill] B.S.P.) as proxies for climatic reconstruction. We collected four living riparian black spruce trees and we investigated the inter- and intra-tree correlations for four trees, at two different sampling heights (1 and 4m), for their carbon (δ13C) and oxygen (δ18O) isotopes, as a test for potential long-term reconstruction. A significant correlation (Pearson coefficient) for the isotopic series was found for the two sampling heights (r=0.92 for δ13C; 0.65 for δ18O), and between the four trees. We further assessed the climatic significance of the mean of the four trees. The strongest correlation of the δ13C series was with the mean of June to August vapor pressure deficit (VPD; r=0.50), and the δ18O values with the June to August climatic index and June to July maximal temperature (r=−0.61 and 0.55, respectively). This study suggests that δ18O series of riparian black spruce trees, and eventually their sub-fossil counterparts, can be used as proxies for reconstructing long climatic series in northeastern Canada.