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Complex network theory provides an important tool for the analysis of complex systems such as the Earth’s climate. In this context, functional climate networks can be constructed using a spatiotemporal climate dataset and a suitable time series distance function. The resulting coarse-grained view on climate variability consists of representing distinct areas on the globe (i.e., grid cells) by nodes and connecting pairs of nodes that present similar time series. One fundamental concern when constructing such a functional climate network is the definition of a metric that captures the mutual similarity between time series. Here we study systematically the effect of 29 time series distance functions on functional climate network construction based on global temperature data. We observe that the distance functions previously used in the literature commonly generate very similar networks while alternative ones result in rather distinct network structures and reveal different long-distance connection patterns. These patterns are highly important for the study of climate dynamics since they generally represent pathways for the long-distance transportation of energy and can be used to forecast climate variability on subseasonal to interannual or even decadal scales. Therefore, we propose the measures studied here as alternatives for the analysis of climate variability and to further exploit their complementary capability of capturing different aspects of the underlying dynamics that may help gaining a more holistic empirical understanding of the global climate system.

The influence of climate conditions in the agricultural environment is important in evapotranspiration, water availability for plants and roots, and other processes. This research focuses on two aspects: (1) the effects of climate change on the occurrence of extreme events that may affect agricultural processes in a region in Illinois (USA), and (2) the effects of climate change on the soil water dynamics in a corn crop. Different climate scenarios developed by the Institut Pierre Simon Laplace, using a climate model with medium resolution—IPSL(CM5MR), provided input to simulate soil water dynamics in two fields with different drainage system layouts. The Hydrus model simulated surface flux and runoff. Results indicate that the variation of precipitation and temperature in the future may increase extreme events, representing a risk for agriculture. Hydrus simulations indicate different results accordingly to the drainage layout, suggesting that it may be necessary to make adjustments in drainage systems in the future. In general, surface flux and runoff will increase over time, and these changes are more related to extreme events than average values. Extreme event indices show vulnerability in agriculture and will be reflected in changes in the soil water dynamics, and may increase the climatic risk of corn production.

We investigate the impacts of climate changes in corn crop profitability and productivity in Parana (Brazil), using SISDRENA model, considering different spacings between drains. SISDRENA model is used as input, daily precipitation, and potential evapotranspiration, properties of the drainage system, data of crop requirement, and physical and hydrological soil parameters. We use climate data from the Eta model. The baseline period of the simulation is from 1981 to 2005. For the future projections, we use the greenhouse gas emission scenarios RCP4.5 and RCP8.5, from 2046 to 2070. We concluded that there are variations in evapotranspiration over the years in the historical simulation, but in the future, this variability tends to be higher. There is a tendency for increased evapotranspiration in both scenarios in the future. We found a large variability of precipitation in both historical simulation and future projections. Changes in precipitation depend on location. In terms of both profitability and productivity, the 10-m spacing between drains is the most recommended. In historical simulation and future projections, it is found large variability in corn productivity over the years. We found that there is no agreement between RCP4.5 and RCP8.5, which shows uncertainty in the results. In general, in RCP4.5 the tendency is to increase 3% of productivity (not significant), while in RCP8.5 the tendency is to decrease 1% (significant), compared to the baseline period. This research contributes to better farmer management and decision making, providing recommendations for the best layout for current and future climate, and indicates trends in corn productivity in future scenarios.

The objective of this paper is to investigate the influence of soil conditions (initial moisture and soil texture type) on the seasonal simulations of precipitation extremes over Upper São Francisco River basin in Brazil. The Eta regional climate model is used to produce simulations for the dry and rainy seasons in southeast Brazil between the years 1979 and 2012. These are 4.5-month integrations. Two maps of soil texture type and two initial soil moisture conditions are tested. Eta rainfall simulations are more sensitive to soil texture than to the changes in the initial soil moisture. The runs using the map of 26 soil texture types represent better the climate of South America compared to the simulations using the map of 9 soil texture types. In general, the extreme climatic indices and the total accumulated precipitation for the dry and rainy seasons in Brazil are underestimated by the Eta regional climate model over the study region, which suggests the necessity of tests with convection precipitation schemes to improve the model’s performance.

Sao Tome and Principe is a small insular country in the west coast of Central Africa. The small dimensions of the islands and the limited natural resources put these islands under highly vulnerable to climate change. To assess the possible future impacts and risks on their agricultural activities, the high-resolution 4-km downscaled climate change projections using Eta regional climate model are used. A crop risk index (CRI) is proposed to assess the risk of climate change on cocoa (Theobroma cacaoL.), pepper (Piper nigrumL. andPiper guinesseL.), taro (Colocasia esculenta(L.) Schott), and maize (Zea maysL.). The index takes into account the vulnerability to climate conditions and the crop yield in the future, and it is classified intovery-high,high,moderate,low, andvery-low. The climate change projections indicate increase in the risk of taro crop, partly due to thermal stress and partly due to the susceptibility to the leaf blight crop disease in taro. The risk of production of the pepper crop is very-high, mainly due to water stress. In mountain regions, the greater risk is due to the thermal stress caused by low temperatures. The cocoa crop is at risk due to water stress, mainly in the northwestern part of the Sao Tome Island, where major local production occurs. The projection indicates increase of the area with very-high risk to maize crops due to the increase of thermal stress and susceptibility to rust. In addition, in parts of the coastal regions, the risk changed from very-low to high risk, due to the low productivity potential. In general, the risks of the four major crops of Sao Tome and Principe increase in the future climate conditions.

The influence of climate conditions in the agricultural environment is important in evapotranspiration, water availability for plants and roots, and other processes. This research focuses on two aspects: (1) the effects of climate change on the occurrence of extreme events that may affect agricultural processes in a region in Illinois (USA), and (2) the effects of climate change on the soil water dynamics in a corn crop. Different climate scenarios developed by the Institut Pierre Simon Laplace, using a climate model with medium resolution-IPSL(CM5MR), provided input to simulate soil water dynamics in two fields with different drainage system layouts. The Hydrus model simulated surface flux and runoff. Results indicate that the variation of precipitation and temperature in the future may increase extreme events, representing a risk for agriculture. Hydrus simulations indicate different results accordingly to the drainage layout, suggesting that it may be necessary to make adjustments in drainage systems in the future. In general, surface flux and runoff will increase over time, and these changes are more related to extreme events than average values. Extreme event indices show vulnerability in agriculture and will be reflected in changes in the soil water dynamics, and may increase the climatic risk of corn production.

In the face of climate change scenarios, it is important to evaluate the possibility of an increase in the incidence of corn crop diseases and to promote studies aimed at creating mitigation measures. This paper aims to study the impacts that regional climate changes may have on the potential occurrence of corn common rust (Puccinia sorghi), in the region of Castro, Parana (Brazil). The Eta climate model was driven by the global model CanESM2. We use the Historical simulation of the EtaCanESM2 model from 1981 to 2005, and future projections from 2046 to 2070 to simulate the occurrence of common rust. The criteria was adopted to simulate the common rust disease favored in environments with the minimum temperature lower than 8 °C, the maximum temperature higher than 32 °C, average temperature between 16 and 23 °C, and relative humidity higher than 95%. In Brazil, there are two different seasons for corn crop (Normaland Safrinha). Results show that relative humidity and minimum temperature simulated by the model presented good skills, approaching the observed data. Compared to the Historical simulation, the projections show a tendency to increase of maximum and minimum temperature in the future, and a tendency to decrease relative humidity. There is an increase in the number of days with the potential for the occurrence of the disease. The distribution of days with favorable conditions to rust disease tends to change in the future. In the Normaland Safrinhaseasons, there is a tendency to increase the number of days with favorable conditions to common rust occurrence. The influence of planting time is greater in Historical simulation when compared to future scenarios. The Safrinhaseason may present more days with the potential for the occurrence of common rust in the future than the Normalseason.