Historical Temperature Research Articles

Warming of the lower Columbia River, 1853 to 2018

AbstractWater temperature is a critical ecological indicator; however, few studies have statistically modeled century‐scale trends in riverine or estuarine water temperature, or their cause. Here, we recover, digitize, and analyze archival temperature measurements from the 1850s onward to investigate how and why water temperatures in the lower Columbia River are changing. To infill data gaps and explore changes, we develop regression models of daily historical Columbia River water temperature using time‐lagged river flow and air temperature as the independent variables. Models were developed for three time periods (mid‐19th, mid‐20th, and early 21st century), using archival and modern measurements (1854–1876; 1938–present). Daily and monthly averaged root‐mean‐square errors overall are 0.89°C and 0.77°C, respectively for the 1938–2018 period. Results suggest that annual averaged water temperature increased by 2.2°C ± 0.2°C since the 1850s, a rate of 1.3°C ± 0.1°C/century. Increased water temperatures are seasonally dependent. An increase of approximately 2.0°C ± 0.2°C/century occurs in the July–Dec time‐frame, while springtime trends are statistically insignificant. Rising temperatures change the probability of exceeding ecologically important thresholds; since the 1850s, the number of days with water temperatures over 20°C increased from ~5 to 60 per year, while the number below 2°C decreased from ~10 to 0 days/per year. Overall, the modern system is warmer, but exhibits less temperature variability. The reservoir system reduces sensitivity to short‐term atmospheric forcing. Statistical experiments within our modeling framework suggest that increased water temperature is driven by warming air temperatures (~29%), altered river flow (~14%), and water resources management (~57%).

Possible link between temperatures in the seashore and open ocean waters of Peru identified by using new seashore water data

The linkage between environmental conditions in the coastal ocean and the open sea varies greatly by region. It is important to clarify, on an area-by-area basis, what coastal monitoring information reveals about the open ocean and how much predictive information for the open ocean may be applicable to the coastal ocean. The Pacific Ocean off the coast of Peru is a monitoring area for the El Niño/La Niña, an oceanic–atmospheric phenomenon of global importance. However, there are not many reliable data along the Peruvian coast. We deployed a network of 6 logger sites along the Peruvian coast during 2017–2020 and compiled a useful, high-resolution dataset of water temperatures. We examined a possible link between temperatures in the coastal waters of Peru and the open sea by comparing the new dataset with historical temperatures in the open ocean. We confirmed that monthly mean anomalies of seashore water temperatures in coastal Peru were strongly correlated with those of open ocean sea surface temperatures. With one exception, the correlation coefficients ranged from 0.80 to 0.92 and were significant at p < 0.01. This result suggested that data obtained from monitoring along the Pacific coast of Peru could be used to indicate the state of the open ocean and that El Niño forecasts for the open ocean could be applied to coastal forecasting as well. Spectral analysis revealed that the periods of changes of seashore water temperature peaked at 80 and 120 days in the region north of 5° S. This result suggested that coastal monitoring might capture intraseasonal dynamics of equatorial Kelvin waves. The absence of clear peaks south of 5° S implied that equatorial wave energy did not penetrate far into off-equatorial regions along the Peruvian coast on intraseasonal timescales.

Better-constrained climate sensitivity when accounting for dataset dependency on pattern effect estimates

Abstract. The best estimate of equilibrium climate sensitivity (ECS) constrained based on the instrumental record of historical warming becomes coherent with other lines of evidence when the dependence of radiative feedback on the pattern of surface temperature change (pattern effect) is incorporated. Pattern effect strength is usually estimated with atmosphere-only model simulations forced with observed historical sea-surface temperature (SST) and sea-ice change and constant pre-industrial forcing. However, recent studies indicate that pattern effect estimates depend on the choice of SST boundary condition dataset, due to differences in the measurement sources and the techniques used to merge and construct them. Here, we systematically explore this dataset dependency by applying seven different observed SST datasets to the MPI-ESM1.2-LR model covering 1871–2017. We find that the pattern effect ranges from -0.01±0.09 to 0.42±0.10 W m−2 K−1 (standard error), whereby the commonly used Atmospheric Model Intercomparison Project II (AMIPII) dataset produces by far the largest estimate. When accounting for the generally weaker pattern effect in MPI-ESM1.2-LR compared to other models, as well as dataset dependency and intermodel spread, we obtain a combined pattern effect estimate of 0.37 W m−2 K−1 [−0.14 to 0.88 W m−2 K−1] (5th–95th percentiles) and a resulting instrumental record ECS estimate of 3.2 K [1.8 to 11.0 K], which as a result of the weaker pattern effect is slightly lower and better constrained than in previous studies.

Mother knows best: nest-site choice homogenizes embryo thermal environments among populations in a widespread ectotherm.

Species with large geographical ranges provide an excellent model for studying how different populations respond to dissimilar local conditions, particularly with respect to variation in climate. Maternal effects, such as nest-site choice greatly affect offspring phenotypes and survival. Thus, maternal behaviour has the potential to mitigate the effects of divergent climatic conditions across a species' range. We delineated natural nesting areas of six populations of painted turtles (Chrysemys picta) that span a broad latitudinal range and quantified spatial and temporal variation in nest characteristics. To quantify microhabitats available for females to choose, we also identified sites within the nesting area of each location that were representative of available thermal microhabitats. Across the range, females nested non-randomly and targeted microhabitats that generally had less canopy cover and thus higher nest temperatures. Nest microhabitats differed among locations but did not predictably vary with latitude or historic mean air temperature during embryonic development. In conjunction with other studies of these populations, our results suggest that nest-site choice is homogenizing nest environments, which buffers embryos from thermally induced selection and could slow embryonic evolution. Thus, although effective at a macroclimatic scale, nest-site choice is unlikely to compensate for novel stressors that rapidly increase local temperatures. This article is part of the theme issue 'The evolutionary ecology of nests: a cross-taxon approach'.

Time-variations of the climate feedback parameter λ are associated with the Pacific Decadal Oscillation

Climate models suggest that the climate feedback parameter λ, which denotes the magnitude of the Earth radiative response to a change in global surface temperature, varies with time. This is because λ depends on the pattern of sea-surface temperature. However, the time-variability of λ and its relation to the sea-surface temperature pattern has not been evaluated in multi-decadal observations. Here, using up-to-date observations, we evaluate the global energy budget over successive 25-year windows and derive a time-series of λ over 1970–2005. We find λ varied within the range [−3.2, −1.0]W ⋅ m−2 ⋅ K−1 since 1970. These variations are linked to the sea-surface temperature pattern changes associated with the Pacific Decadal Oscillation. Climate model simulations forced with observations of historical sea-surface temperature show a 1970–2005 mean λ that is consistent with observations. However, they fail in reproducing observed λ time-variations since 1970 which are associated to the Pacific Decadal Oscillation, meaning that climate models underestimate the pattern effect at decadal time scales.

A Comparative Study of Using Adaptive Neural Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), and SMRGT Models in Flow Coefficient Estimation

Estimating the flow coefficient is a crucial hydrologic process that plays a significant role in flood forecasting, water resource planning, and flood control. Accurate prediction of the flow coefficient is essential to prevent flood-related losses, manage flood warning systems, and control water flow. This study aimed to predict the flow coefficient for a period of 19 years (2000-2019) in the Aksu River Sub-Basin in Turkey, using historical climatic data, including precipitation, temperature, and humidity, provided by The Turkish State of Meteorological Service (TSMS). The study utilized three different approaches, namely, the Adaptive Neural Fuzzy Inference System (ANFIS), Simple Membership function and fuzzy Rules Generation Technique (SMRGT), and Gaussian Process Regression (GPR), to predict the flow coefficient. The models were evaluated using several statistical tests, such as Root Mean Square Error (RMSE), Coefficient of Determination (R2), Mean Absolute Error (MAE), and Mean Square Error (MSE), to determine their accuracy. Based on the evaluation criteria, it is concluded that the Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model has superior flow coefficient estimation performance than the other models.

On the statistical distribution of temperature and the classification of extreme events considering season and climate change—an application in Switzerland

With the increased occurrence of hot spells in recent years, there is growing interest in quantifying the recurrence of extreme temperature events. However, pronounced temperature anomalies occur all year round, and a reliable classification in terms of the time of occurrence in the year is needed. In this study, we present a novel approach to classifying daily air temperatures that take into account the seasonal cycle and climate change. We model the distribution of the daily Swiss temperatures using the skewed generalized error distribution with four time-varying parameters, thereby accounting for non-Gaussianity in daily air temperature, while the climatic trend is modeled linearly with smoothed northern hemisphere temperature as an explanatory variable. The daily observations are then transformed into a standard normal distribution. The resultant standardized temperature anomalies are comparable within a year and between years and are used for quantile-based empirical classification. The approach is suitable to classify historical and current extreme temperatures with respect to the temperature range expected at the time of the event. For example, a heat wave occurring at the end of June is classified as less likely to occur than a heat wave of similar intensity occurring in mid-July, as is shown for the two 7-day heat waves that struck Switzerland in the summer of 2019. Furthermore, climate change has increased the probability of hot events and decreased the probability of cold events in recent years. The presented approach thus allows a fair classification of extreme temperatures within a year and between years and offers new possibilities to analyze daily air temperature.

Using historical catch data to evaluate predicted changes in fish relative abundance in response to a warming climate

Using models to predict future changes in species distributions in response to projected climate change is a common tool to aid management and species conservation. However, the assumption underlying this approach, that ecological processes remain stationary through time, can be unreliable, and more empirical tests are needed to validate predictions of biotic outcomes of global change. The scarcity of reliable historical and long‐term datasets can make these tests difficult. Moreover, incorporating abundance and multiple sampling methods can improve model predictions and usability for management. Our study 1) provides insight into how well models can predict environmental change under a warming climate, 2) incorporates multiple sampling gears and abundance data in modeling to better capture changes in populations and 3) shows the value of historical datasets for improving predictive models of population change. We used contemporary (2003–2019) and historical (1936–1964) abundance datasets of the North American fish largemouth bass Micropterus salmoides in lakes across the state of Michigan, USA. We developed Bayesian hierarchical models that leverage the use of multiple gears in contemporary lake surveys to estimate the relative catchabilities of largemouth bass for each gear and hindcast the models to predict historical abundance. Our estimates of relative density change over time were correlated with temperature change over time; increasing surface water temperature led to increasing largemouth bass density. Hindcasting models to historical lake temperatures performed similarly in predicting historical density to models predicting contemporary density. Our results suggest that models built using spatial environmental gradients can reliably predict population changes through time. Understanding the sampling methods and the environmental context of observational datasets can help researchers test for potential sampling biases and identify confounding factors that will improve predictions of future impacts of environmental change.

Climate resilience of dry season cereals in India

India is the world’s second largest producer of wheat, with more than 40% increase in production since 2000. Increasing temperatures raise concerns about wheat’s sensitivity to heat. Traditionally-grown sorghum is an alternative rabi (winter season) cereal, but area under sorghum production has declined more than 20% since 2000. We examine sensitivity of wheat and sorghum yields to historical temperature and compare water requirements in districts where both cereals are cultivated. Wheat yields are sensitive to increases in maximum daily temperature in multiple stages of the growing season, while sorghum does not display the same sensitivity. Crop water requirements (mm) are 1.4 times greater for wheat than sorghum, mainly due to extension of its growing season into summer. However, water footprints (m3 per ton) are approximately 15% less for wheat due to its higher yields. Sensitivity to future climate projections, without changes in management, suggests 5% decline in wheat yields and 12% increase in water footprints by 2040, compared with 4% increase in water footprint for sorghum. On balance, sorghum provides a climate-resilient alternative to wheat for expansion in rabi cereals. However, yields need to increase to make sorghum competitive for farmer profits and efficient use of land to provide nutrients.

Western pine beetle voltinism in a changing California climate

Abstract Recent hot droughts in California resulted in ponderosa pine (Pinus ponderosa) mortality attributed to drought and western pine beetle (WPB, Dendroctonus brevicomis). While drought alone can cause tree death, direct warming effects on WPB are a contributing factor. Research on WPB generation timing (voltinism), however, is lacking. We monitored WPB tree attacks and adult emergence timing at two California sites and developed a degree‐day model from field‐observed data. Historical, contemporary, and future temperatures for several California sites were used with the model to examine trends in WPB voltinism. Field data showed a single summer and an overwinter generation at a northern California site. As summer temperatures increased beyond 1900–1980 averages, the predicted number of full and partial WPB generations by 2021 had increased from ~2 annual (one summer and one overwinter) generations historically to ~2.3 at two northern California sites and from ~2.3 to ~3.2 at two warmer California sites. Historical and contemporary data suggest winter warming was not sufficient for an additional generation overwinter. Instead, increases in generations were driven by summer and fall temperatures. Unconstrained increases in the number of future annual generations will be limited by complex, but not well understood, WPB thermal adaptations. Increased knowledge of temperature‐driven WPB population growth will improve forest vegetation models aimed at predicting ponderosa pine mortality in a changing climate.

Historical temperature and wind conditions in the Hudson Strait region from 1880 to 1950: Kangiqsujuaq, Quaqtaq, and Killiniq

Historical temperature and wind conditions in the Hudson Strait region from 1880 to 1950: Kangiqsujuaq, Quaqtaq, and Killiniq

Economic–Financial Assessment of Seawater Desalination Plants in Northern Chile to Reduce Hydric Scarcity and a Proposal for the Environmental and Sustainable Use of Brine Waste by Cultivating the Microalga Dunaliella salina to Produce β-Carotene

Climate change and global warming generate serious consequences and disturbances by drastically modifying historical temperature and precipitation patterns. Water scarcity is one of the most revealing phenomena of these instabilities. This transdisciplinary bibliometric and economic–financial research focuses on analyzing two aspects: first, the feasibility of implementing seawater desalination plants as a solution to water scarcity in northern Chile. Investment and amortization costs of the desalination plants were determined (NPV-IRR-IRP). NPV showed a positive value indicating a recovery of the initial investment and a surplus over profitability. The IRR was higher than the discount rate calculated for NPV, which showed that the investment project was accepted. The IRP indicated that the initial investment of the plant would be recovered in 3.7 years. Second, an innovative and environmentally sustainable solution to the brine (NaCl) waste generated by desalination plants is proposed through the cultivation of Dunaliella salina microalgae tolerant to high brine concentrations to produce β-carotene. The analyzed desalination plants and the sustainable use of brine residues offer interesting economic perspectives to a 10-year projection establishing a surplus over profitability. The SWOT analysis estimates an excellent production of β-carotene through the microalgae and alternatives to the problem of sea pollution by concentrated brine waste.

Shifts in students’ predictive reasoning from data tables in years 3 and 4

In this exploratory descriptive study, changes in one cohort’s responses to an authentic statistical investigation at the commencement of years 3 and 4 were analysed. Forty-four students made predictions by interpreting a data table of historical monthly temperatures, represented these data and explained their reasoning. An Awareness of Mathematical Pattern and Structure (AMPS) framework was extended to analyse students’ responses at five increasing levels of predictive reasoning. More developed predictive reasoning was observed in year 4 than for year 3, as well as large individual differences in both years. Most year 4 students (87%) made predictions within the historical range, relative to half the same cohort in year 3 (54%). More year 4 students (79%) made predictions based on extraction, clustering and aggregation of these data than those in year 3 (51%). Year 4 students noticed patterns such as seasonal trends and variability in these data and observed measures of central tendency. By year 4, 57% of students’ representations demonstrated transnumeration using extracted data from the table, including pictorial, column and line graphs. However, most students’ representations and explanations of these data lagged behind their predictions at both year levels.

Quantifying the contribution of major carbon producers to increases in vapor pressure deficit and burned area in western US and southwestern Canadian forests

Increases in burned forest area across the western United States and southwestern Canada over the last several decades have been partially driven by a rise in vapor pressure deficit (VPD), a measure of the atmosphere’s drying power that is significantly influenced by human-caused climate change. Previous research has quantified the contribution of carbon emissions traced back to a set of 88 major fossil fuel producers and cement manufacturers to historical global mean temperature rise. In this study, we extend that research into the domain of forest fires. We use a global energy balance carbon-cycle model, a suite of climate models, and a burned area (BA) model to determine the contribution of emissions traced to the major carbon producers to the long-term increase in VPD during 1901–2021 and to cumulative forest fire area during 1986–2021 in the western US and southwestern Canada. Based on climate model data, we find that emissions traced to these carbon producers contributed 48% (interquartile range (IQR) 38%–63%) of the long-term rise in VPD between 1901 and 2021. BA modeling indicates that these emissions also contributed 37% (IQR 26%–47%) of the cumulative area burned by forest fires between 1986 and 2021 in the western US and southwestern Canada. The increase in VPD in this region is linked to both increased fire activity and the region’s current and prolonged megadrought. As loss and damage from these hazards mounts, this research can inform public and legal dialogues regarding the responsibility carbon producers bear for addressing past, present, and future climate risks associated with fires and drought in the western US and southwestern Canada.

Experimental Study on Boiling Vaporization of Liquid Hydrogen in Nonspreading Pool

Research on the boiling vaporization process of liquid hydrogen spilled on solid ground is very important for the safety risk assessment of liquid hydrogen. Since the main source of the heat flux in the vaporization process comes from the ground, the heat flux from the ground into the liquid pool should be studied in-depth. In this paper, the boiling vaporization process of liquid hydrogen on the surface of concrete is studied. The analysis of the boiling process of a liquid pool is conducted by utilizing the boiling curve and historical temperature data collected in close proximity to the surface of the concrete. It was found that the boiling regime of a liquid hydrogen pool on the concrete surface presents non-uniformity, and the film boiling of liquid hydrogen on the concrete surface ended earlier than the results calculated by boiling regime correlations. When the measured temperature in the experiment indicates a transition from film boiling to the transition boiling, the temperature difference between the thermocouple temperature measured at a depth of 2 mm and the boiling point of liquid hydrogen is 130 K higher than the predicted superheat of the minimum heat flux (MHF). In the later stage of the experiment, the average relative error between the experimental value of the vaporization rate and the predicted value of the model is 7.48%. This research advances the understanding of heat transfer between concrete ground and a liquid hydrogen pool. In addition, the experimental data obtained in this study contributes to improving the source term model for safety analysis of liquid hydrogen spills.

Using seasonal climate scenarios in the ForageAhead annual forage production model for early drought impact assessment

AbstractHigh interannual variability of forage production in semiarid grasslands leads to uncertainties when livestock producers make decisions, such as buying additional feed, relocating animals, or using flexible stocking. Within‐season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibly with higher confidence. In this study, we use a recently developed forage production model, ForageAhead, that uses environmental and seasonal climate variables to estimate the annual forage production as approximated by remotely sensed vegetation data. Because, among other variables, this model uses observed summer climate data, the model output cannot be produced early enough in the year (e.g., spring months) to inform within‐season management decisions. To address this issue, we developed summer climate scenarios (e.g., extremely warm and dry and moderately cool and wet) that serve as an input in the model in combination with observed winter and spring climate data from a particular year. The summer climate scenarios used historical summer precipitation and temperature data (1950–2018) categorized into three, five, and seven percentile categories. These percentile values were then combined to represent summer climate scenarios, which were further used as the ForageAhead model input. We tested the optimal number of percentile categories to be used as the model input to obtain accurate prediction of forage production while also minimizing the number of possible temperature and precipitation combinations, which increases with the number of percentile categories. For the 19‐year period analysis (2000–2018), we also determined the most and least common scenarios that occurred in the western United States. When using five percentile categories for summer precipitation and temperature, we were able to capture the interannual variability in the spatial extent of abnormally low and high biomass production. The ForageAhead predictions captured similar spatial patterns of forage anomalies as another similar model (Grass‐Cast). This method can be made available in a user‐friendly automated system that can be used by livestock producers and rangeland managers to inform within‐season management decisions. This method can be especially valuable for flexible stocking as it provides a range of possible annual forage production scenarios by the end of May.

Farmers’ perceptions of climate variability and adaptation strategies in the rural areas of Dire Dawa administration, eastern Ethiopia

Climate variability has significant impact on agricultural production especially in low-income countries where agriculture largely relies on rainfall, but only a few studies explored this issue at local scale. Therefore, this study was conducted to characterize local climate and assess farmers' perceptions and adaptation strategies to climate variability in the rural areas of Dire Dawa administration. Historical rainfall and temperature data (1987–2017) were obtained from National Meteorological Agency (NMA) of Ethiopia, while data of farmers' perceptions and adaptation strategies were collected from a total of 120 household heads through survey questionnaire, key informant interviews and focus group discussions. The results revealed that the area received an average annual rainfall of 568.3 mm with main rainy season (kiremt) contributing 70.7% to annual rainfall. The earliest and latest onset dates of kiremt season were 15th of April and 2nd of August, respectively. The amount of annual and kiremt rainfall totals showed low and medium variability with a coefficient variability (CV) of 18.3% and 27.7%, respectively, whereas short rainy season (belg) rainfall had high variability with a CV of 43.9%. Climate variability perception analysis showed that an overwhelming majority of the respondents (90%) perceived a decrease in the annual rainfall and 91.7% detected an increase in annual average temperature in the study area. Farmers of the study area were well aware of the changes in rainfall and temperature and thus employed a range of adaptation practices. Soil and water conservation practices (100%), off-farm income diversification (63%), planting drought-tolerant varieties (50%) and changing of planting date (45%) were the main adaptation strategies employed in the study area to avert the negative effects of climate variability. The findings imply that the area has been experiencing palpable changes in climate variables during the study period against which farmers exercised multiple adaptation strategies. However, farmers in the area are still face hardship as a result of climate variability which necessitates improving farmers’ resilience through innovative mechanisms and better extension services.

Water-Mass Coordinates Isolate the Historical Ocean Warming Signal

Abstract Persistent warming and water cycle change due to anthropogenic climate change modifies the temperature and salinity distribution of the ocean over time. This “forced” signal of temperature and salinity change is often masked by the background internal variability of the climate system. Analyzing temperature and salinity change in water-mass-based coordinate systems has been proposed as an alternative to traditional Eulerian (e.g., fixed-depth, zonally averaged) coordinate systems. The impact of internal variability is thought to be reduced in water-mass coordinates, enabling a cleaner separation of the forced signal from background variability—or a higher “signal-to-noise” ratio. Building on previous analyses comparing Eulerian and water-mass-based one-dimensional coordinates, here we recast two-dimensional coordinate systems—temperature–salinity (T–S), latitude–longitude, and latitude–depth—onto a directly comparable equal-volume framework. We compare the internal variability, or “noise” in temperature and salinity between these remapped two-dimensional coordinate systems in a 500-yr preindustrial control run from a CMIP6 climate model. We find that the median internal variability is lowest (and roughly equivalent) in T–S and latitude–depth space, compared with latitude–longitude coordinates. A large proportion of variability in T–S and latitude–depth space can be attributed to processes that operate over a time scale greater than 10 years. Overall, the signal-to-noise ratio in T–S coordinates is roughly comparable to latitude–depth coordinates, but is greater in regions of high historical temperature change. Conversely, latitude–depth coordinates have greater signal-to-noise ratio in regions of historical salinity change. Thus, we conclude that the climatic temperature change signal can be more robustly identified in water-mass coordinates. Significance Statement Changes in ocean temperature and salinity are driven both by human-induced climate change and by modes of natural variability in the climate system, such as El Niño–Southern Oscillation. It can be difficult to isolate the human-induced “signal” of climate change from the natural fluctuations or “noise” in the climate system. Water-mass-based methods, which “follow” a parcel of water around the ocean, have been thought to improve on “Eulerian” (i.e., analyses performed at fixed latitude, longitude, and depth) frames of reference as they are less impacted by the noise. However, it is difficult to cleanly compare between water-mass-based methods and Eulerian methods. Here, we aim to quantify the extent to which water-mass-based frameworks improve on Eulerian frameworks in isolating the climate signal from the noise. We recast water-mass and Eulerian methods onto an equivalent grid, enabling a clean comparison between them, and find that doing so increases the signal-to-noise ratio in water-mass-based coordinates in regions of ocean warming. These results emphasize the utility of water-mass-based methods in analyzing long-term climatic temperature change in the ocean.

Long-term projections of the impacts of warming temperatures on Zika and dengue risk in four Brazilian cities using a temperature-dependent basic reproduction number.

For vector-borne diseases the basic reproduction number [Formula: see text], a measure of a disease's epidemic potential, is highly temperature-dependent. Recent work characterizing these temperature dependencies has highlighted how climate change may impact geographic disease spread. We extend this prior work by examining how newly emerging diseases, like Zika, will be impacted by specific future climate change scenarios in four diverse regions of Brazil, a country that has been profoundly impacted by Zika. We estimated a [Formula: see text], derived from a compartmental transmission model, characterizing Zika (and, for comparison, dengue) transmission potential as a function of temperature-dependent biological parameters specific to Aedes aegypti. We obtained historical temperature data for the five-year period 2015-2019 and projections for 2045-2049 by fitting cubic spline interpolations to data from simulated atmospheric data provided by the CMIP-6 project (specifically, generated by the GFDL-ESM4 model), which provides projections under four Shared Socioeconomic Pathways (SSP). These four SSP scenarios correspond to varying levels of climate change severity. We applied this approach to four Brazilian cities (Manaus, Recife, Rio de Janeiro, and São Paulo) that represent diverse climatic regions. Our model predicts that the [Formula: see text] for Zika peaks at 2.7 around 30°C, while for dengue it peaks at 6.8 around 31°C. We find that the epidemic potential of Zika will increase beyond current levels in Brazil in all of the climate scenarios. For Manaus, we predict that the annual [Formula: see text] range will increase from 2.1-2.5, to 2.3-2.7, for Recife we project an increase from 0.4-1.9 to 0.6-2.3, for Rio de Janeiro from 0-1.9 to 0-2.3, and for São Paulo from 0-0.3 to 0-0.7. As Zika immunity wanes and temperatures increase, there will be increasing epidemic potential and longer transmission seasons, especially in regions where transmission is currently marginal. Surveillance systems should be implemented and sustained for early detection.

Interdependence between climate change and migration: Does agriculture, geography, and development level matter in sub‐Saharan Africa?

AbstractConcerns about the human effects of climate change have contributed to forecasts of how populations in drought‐prone and flood‐prone areas would respond to these events. Empirical studies have predicted that human migration has been among the critical resilient strategy in responding to the impact of climate change. To obtain a more comprehensive understanding of the climate–migration relationship, the impacts of climate change on international migration flows from sub‐Saharan Africa nations to South Africa are investigated empirically in this paper. The study employed static and dynamic models and panel data from 35 countries in sub‐Saharan Africa, spanning 1990 to 2017. The findings are as follows: (1) The analysis shows that historical temperature has a positive and statistically significant effect on outmigration in agriculture‐dependent nations. (2) The analysis shows that agricultural value added as a share in gross domestic product has a negative and statistically significant effect on outmigration in agriculture‐dependent nations. (3) The results also show that geographic location and development level of a country, in addition to dependency on agriculture, are key factors in the climate change–international migration nexus. Policy implications are discussed.