Search

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

In this paper, 16 cities representing four major architectural climate zones of China were selected, and the heating and cooling loads of office building during the period of 1961–2017 were simulated by using TRNSYS software. Daily extreme heating and cooling loads were determined by percentile methods, and the climate change impact on extreme loads was analyzed. The results showed that the days of extreme heating load of office building significantly decreased in almost all the selected cities, whereas there were no significant changes in the days of extreme cooling load. The daily extreme heating load was dominantly affected by the temperature (R2 = 0.43–0.79), but the daily extreme cooling load was related to multiple climate factors, including temperature, relative humidity, and solar radiation. Single temperature can only explain 3–37% of the changes of daily extreme cooling load, and the combination of multiple climate factors can explain 58–85%. Importantly, the effect of temperature on extreme cooling load showed decrease from the cold to hot climate zones, but the impacts of relative humidity and solar radiation gradually increased. These results suggest that the maximum design capacity of heating systems can properly decrease, and the design capacity of cooling systems may not largely increase with the climate warming. In addition, the combined effect of multiple climate factors rather than the single temperature, as well as the different responses of extreme cooling energy consumption to climate change in different climate zones, should be considered when making measures for improving operating efficiency of air-conditioning system. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental questions on climate change and variability: to what extent has climate changed and/or varied over years in two districts of different agro-ecological regions or zones and how do any changes differ between the zones or districts? Given the rural-rural migration pattern observed between the districts, understanding climate change risk to rural livelihoods cannot be overemphasised. To assess change and variability, we utilise rainfall data-records over a 36-year period from 1980 to 2016. Results show that there are wide variations and differences within and between the districts. Evidence suggests a general reduction in both annual rainfall and wet days. There is also ground to suggest that the rainy season duration is becoming shorter, given that rainfall onset is increasingly starting late, while cessation is increasingly coming early. Dry spells frequency and duration trends within rainy season show an increase over the period examined. We conclude that local climate in both areas has changed over the period investigated. However, while Livingstone seems to have experienced more droughts and unreliable rainfall, Kabwe experienced a bigger change in both rainfall and rainy season duration. We further conclude that migrants into Kabwe and other inhabitants are not any safer from climate change risk. [ABSTRACT FROM AUTHOR]

Climate change is now unequivocal; however, the type and extent of terrestrial impacts are still widely debated. Among these, the effects on slope stability are receiving a growing attention in recent years, both as terrestrial indicators of climate change and implications for hazard assessment. High-elevation areas are particularly suitable for these studies, because of the presence of the cryosphere, which is particularly sensitive to climate. In this paper, we analyze 358 slope failures which occurred in the Italian Alps in the period 2000–2016, at an elevation above 1500 m a.s.l. We use a statistical-based method to detect climate anomalies associated with the occurrence of slope failures, with the aim to catch an eventual climate signal in the preparation and/or triggering of the considered case studies. We first analyze the probability values assumed by 25 climate variables on the occasion of a slope-failure occurrence. We then perform a dimensionality reduction procedure and come out with a set of four most significant and representative climate variables, in particular heavy precipitation and short-term high temperature. Our study highlights that slope failures occur in association with one or more climate anomalies in almost 92% of our case studies. One or more temperature anomalies are detected in association with most case studies, in combination or not with precipitation (47% and 38%, respectively). Summer events prevail, and an increasing role of positive temperature anomalies from spring to winter, and with elevation and failure size, emerges. While not providing a final evidence of the role of climate warming on slope instability increase at high elevation in recent years, the results of our study strengthen this hypothesis, calling for more extensive and in-depth studies on the subject. [ABSTRACT FROM AUTHOR]

For hundreds of years, human activity has modified the planet's surface through land-use practices. Policies and decisions on how land is managed and land-use changes due to replacement of forests by agricultural cropping and grazing lands affect greenhouse gas emissions. Agricultural management and agroforestry and the resulting changes to the land surface alter the global carbon cycle as well as the Earth's surface albedo, both of which in turn change the Earth's radiation balance. This makes land-use change the second anthropogenic source of climate change after fossil fuel burning. However, the scientific research community has so far not been able to identify the direction and magnitude of the global impact of land-use change. This paper examines the effects of net carbon flux from land-use change on temperature by applying Granger causality and error correction models. The results reveal a significant positive long-run equilibrium relationship between land-use change and the temperature series as well as an opposing short-term effect such that land-use change tends to lead to global warming; however, a rise in temperature causes a decline in land-use change. [ABSTRACT FROM AUTHOR]

In this paper, robust statistical methods (including the climatic tendency ratio, inverse distance weight (IDW), and Mann-Kendall’s non-parametric statistical tests) were applied to analyze the annual sequences of meteorological data for the period 1960-2014. The results indicated that the mean annual air temperature in the Pan-Hexi and the surrounding regions has increased by 0.9 °C. Over the 55-year period of study, this represented a warming rate that was significantly higher than the contemporaneous warming rate in China and the rest of the world. The warming trend in northeastern Tibet was the most pronounced. Here, the mean warming rate exhibited an increasing trend from the mid-late 1980s, and the cold season-dominated warming changed to year-round warming. The rise in the amplitude of the mean minimum temperature was significant, and the variation in the amplitude of the extreme minimum temperature was greater than that of the extreme maximum temperature, exhibiting a variation period of approximately 10 years. Since 1960, the mean precipitation in the study region and the surrounding areas first increased, and then decreased, and then increased again rapidly from 2007. Precipitation and relative humidity trend showed a 13- and a 10-year variation period, respectively. Crop evapotranspiration exhibited a decreasing trend, but from 1993 onwards, there was an increasing trend and a 9-year variation period was also notable based on observed datasets. In the most recent 55 years (1960-2014), the sunshine duration and mean wind velocity in the study area first showed a decreasing trend followed by an increasing trend, and then it exhibited a decreasing trend from east to west of the study region. The temporal variations in the climatic elements were similar to those of China, suggesting that the climate change signal in the study region over the last 55 years appeared to be well correlated with the global climate change signal. [ABSTRACT FROM AUTHOR]

Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability. [ABSTRACT FROM AUTHOR]

As a result of climatic change associated with global warming, aridity is an increasing problem in many parts of the world, including south-eastern and southern regions of Romania. This paper clarifies the concept of aridity, and discusses related concepts including indices of aridity, and their influence on some landscape and soil features including climatic water deficit (WD) and the depth to soil carbonates (DC). As used here, WD is calculated as the difference between precipitation sum (P) and the Penman-Monteith reference evapotranspiration sum (ETo-PM) over certain periods. Another three well-known aridity indices are also considered: De Martonne’s index (Iar-DM), Thornthwaite’s index (Iar-TH), the UNESCO (1979) P/ETo-PM ratio index (Iar-P/ETo-PM). WD is as high as −450 mm during the growing season in the most arid, south-eastern and southern regions of Romania, especially in the Dobrogea and Baragan areas. In other regions of Romania, including most of the plains and plateaus where agriculture is an important branch of the economy, WD reaches −100 to −300 mm during the growing season. The above aridity indices were spatially interpolated for specific periods by kriging, to generate relatively homogeneous areas. WD can also be seen as an aridity index which has the advantage of a more accurate quantification of the water supply needed for a reference crop, e.g. grass under standardised conditions, for various geographical regions. WD is significantly correlated with the other aridity indexes and with DC. This paper also examines the risk of aridity spreading, and suggests improvements to the water management system for agriculture in Romania. [ABSTRACT FROM AUTHOR]

Mountain observatories around the world are unique sites for monitoring and investigating variations, trends, forcings and feedbacks in the climate system, which are of utmost interest for understanding global climate change. From the small number of these research platforms, Sonnblick Observatory (Austrian Alps) stands out because of its long time series dating back to 1886 at an elevation of 3,100 m a.s.l. The paper describes the contribution of mountain observatories to climatology by the example of Sonnblick Observatory. The observatory's scientific evolution is summarised, starting from the original idea of upper air atmospheric measurements to its recent role as an atmospheric background station and interdisciplinary research site. [ABSTRACT FROM AUTHOR]

Many areas of society are susceptible to the effects of extreme temperatures. Without an adequate definition of what constitutes heat and cold waves, it is impossible to assess either their changes in the past or their possible consequences for the future. The Intergovernmental Panel on Climate Change recommended criteria for heat wave duration indexes based on two arbitrarily defined constants. The principal weakness of this approach is that it does not yield comparable results for different geographical locations. This paper remedies the current lack of a meteorologically based definition of heat and cold waves and offers a preliminary test of its performance. Having previously shown that maximum daily temperature values follow normal frequency distribution, we derive statistical thresholds (e.g., below and above normal) from that distribution. These thresholds are thus climate specific and their change can be compared across geographical locations. These criteria are then tested on the homogeneous time series of maximum daily temperature observed for the period 1961-2008 with respect to three different geographical locations. The results obtained show an increase in the frequency of heat waves for the period 1991-2008 in comparison with the normal climatological period 1961-1990. [ABSTRACT FROM AUTHOR]

Statistical analyses regarding climate studies have often used the average temperature as one of the main variables. However, the tails of the respective distributions are also crucial and have become increasingly considered in recent years. As the Intergovernmental Panel on Climate Change in its fourth assessment report (IPCC ) states, 'the type, frequency and intensity of extreme events are expected to change as Earth's climate changes'. In this paper, the focus is on the statistical behaviour of extreme (maximum and minimum) values of temperature, both in winter and summer. Under the framework of the Extreme Value Theory, the methodology of block maxima is employed. The generalised extreme value distribution, allowing for a linear trend in the location parameter, is fitted to data in order to capture the time tendency in the non-stationary processes. We are able to approximate expected values with a determined probability and to identify time trends of such events. Particularly, an increasing time trend in maximum and minimum temperature is generally detected which could be of great concern to public and private sectors. [ABSTRACT FROM AUTHOR]

This paper discusses patterns of annual and monthly precipitation variability at seven weather stations in east central Europe (1851–2007). Precipitation patterns were compared to three simple regional indices of atmospheric circulation, i.e., western circulation, southern circulation and the cyclonicity (C) index and a relationship between precipitation and the North Atlantic Oscillation index was identified. Correlations of the monthly records and multiple regression, using a principal components’ analysis, helped determine the statistical significance of the dependence of precipitation on the circulation indices. The Mann–Kendall test revealed no trend to change in any of the precipitation series, but a certain spatial regularity could be discerned in the phase of the annual periodic component. A common feature of the variability in central European annual precipitation is the dry period identified in the 1980s and the first half of the 1990s. In the northern part of the region, above-average precipitation was noted from the 1960s through to the mid-1970s as a result of the frequent prevalence of depressions. South of the divide, the wettest period was recorded at the turn of 1930s/1940s. After a number of very wet years in the last decade of the twentieth century and the beginning of the twenty-first century, precipitation began to fall at all of the region’s weather stations. The C index is the strongest circulation-linked factor influencing precipitation in central Europe and it accounts for more than 40% of the variance in spatially averaged wintertime precipitation. [ABSTRACT FROM AUTHOR]

Meteorological and oceanographic data from ships of opportunity are the largest contributor to the world’s ocean surface database and thus are extensively used to estimate the change in climatic properties over the world’s oceans during the previous 150 years. The importance of these data for climate change studies underscores the need to fully understand the error associated with averages of these data. The sampling error problem is especially acute for ship data due to the fact that ships are moving platforms and, thus, report observations from constantly varying locations with time. This paper develops a theoretical framework for assessing the averaged sampling error associated with monthly, 1°×1° latitude-longitude box averaged ship data. It should be noted that the time-space distribution of ships within the averaging domain strongly affects the sampling error. This is shown in our derivation. The framework developed here can be used to improve upon existing methods for estimating the sampling error associated with three-dimensional box averages of meteorological and oceanographic data obtained from ship records. The framework is complimentary to existing methods of assessing biases and random error due to instrumentation, recording, etc. It is demonstrated mathematically that the uncertainty due to incomplete sampling is primarily a trade off between of the number of observations and their relative locations within the box as well as the inherent time-space correlation structure of the variable of interest. This work differs from other studies in that the three-dimensional interdependence of data is taken into account in deriving an expression for the sampling error. [ABSTRACT FROM AUTHOR]

Significant changes in the climatic system have been observed, which may be attributed to human-enhanced greenhouse effect. Even stronger changes are projected for the future, impacting in an increasing way on human activity sectors. The present contribution, prepared in the framework of the MICE (Modelling the Impact of Climate Extremes) Project of the European Union, reviews how climate change may impact on winter tourism in the Alpine region, intense precipitation and flood potential in central Europe, forest damage in Scandinavia and beach holidays in the Mediterranean coast. Impacts are likely to be serious and largely adverse. Due to a lack of adequate information and lack of broadly accepted and reliable mathematical models describing the impact of changes in climate extremes on these activity sectors, it has been found useful to use expert judgement based impact assessment. Accordingly, regional mini-workshops were organized serving as platforms for communication between scientists and stakeholders, vehicles for dissemination of the state-of-the-art of the scientific understanding and for learning stakeholders’ view on extreme events, their impacts and the preparedness system. Stakeholders had the opportunity to react to the scientific results and to reflect on their perception of the likely impacts of projected changes in extremes on relevant activity sectors and the potential to adapt and avert adverse consequences. The results reported in this paper present the stakeholders’ suggestions for essential information on different extreme event impacts and their needs from science. [ABSTRACT FROM AUTHOR]