Future Climate Variability Research Articles

Modeling the impact of changing in climatic variables on streamflow of Borkena River catchment, Awash Basin, Ethiopia

ABSTRACT Poor land management coupled with increasing climate extremes is affecting the livelihoods of Ethiopian communities. So, evaluating the possible impact of climate change on water resource is essential for planning a sustainable water use system. As a result, this study aimed to investigate the impact of climatic variable changes on streamflow in the Borkena River Catchment, Awash Basin, Ethiopia, using projected climate data of canESM2 (Canadian Earth System Model of second generation) global climate model. Future scenario, analysis was performed for the 2020s, 2050s and 2080s for the Representative Concentration Pathway of RCP4.5 and RCP8.5. Impact assessment of climatic variable changes on streamflow was done by HBV Light hydrological models. The model was calibrated using a semi-automated method and the performance was measured by the coefficient of determination (R 2), Nash–Sutcliffe efficiency (NSE) and mean difference. The performance of HBV Light hydrological model during the calibration and validation period showed a satisfactory agreement between observed and simulated flow with R 2, NSE and mean difference. The value of R 2, NSE and mean differences was 0.84, 0.82 and 0 for calibration and 0.83, 0.78 and 8.23 for validation periods, respectively. The downscaled precipitation and temperature result reveals decrease and increase value in all future three-time horizon for both RCP8.5 and RCP4.5 scenarios in an annual basis respectively. This condition was expected to result decreased in mean annual streamflow of Borkena River in the future three-time horizon with the value of –16.81%,–17.25% and –18.28% for RCP8.5 scenario and –6.88%, –9.89% and –10.66% for RCP4.5 scenario in the 2020s, 2050s and 2080s, respectively. Inoculation future climate variation has a significant impact on the surface water potential of the catchment. So, for sustainable development of the River catchment in water use the streamflow of the catchment should be harvested.

Enhancing flexibility for climate change using seasonal energy storage (aquifer thermal energy storage) in distributed energy systems

Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. The design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.

Barotropic tides in MPAS-Ocean (E3SM V2): impact of ice shelf cavities

Abstract. Oceanic tides are seldom represented in Earth system models (ESMs) owing to the need for high horizontal resolution to accurately represent the associated barotropic waves close to coasts. This paper presents results of tides implemented in the Model for Prediction Across Scales–Ocean or MPAS-Ocean, which is the ocean component within the U.S. Department of Energy developed Energy Exascale Earth System Model (E3SM). MPAS-Ocean circumvents the limitation of low resolution using unstructured global meshing. We are at this stage simulating the largest semidiurnal (M2, S2, N2) and diurnal (K1, O1) tidal constituents in a single-layer version of MPAS-O. First, we show that the tidal constituents calculated using MPAS-Ocean closely agree with the results of the global tidal prediction model TPXO8 when suitably tuned topographic wave drag and bottom drag coefficients are employed. Thereafter, we present the sensitivity of global tidal evolution due to the presence of Antarctic ice shelf cavities. The effect of ice shelves on the amplitude and phase of tidal constituents are presented. Lower values of complex errors (with respect to TPXO8 results) for the M2 tidal constituents are observed when the ice shelf is added in the simulations, with particularly strong improvement in the Southern Ocean. Our work points towards future research with varying Antarctic ice shelf geometries and sea ice coupling that might lead to better comparison and prediction of tides and thus better prediction of sea-level rise and also the future climate variability.

Mudanças Climáticas e a Mancha Foliar de Phoma spp. no Café Arábica: Uma Abordagem de Modelação CMIP6

Abstract Coffee is currently one of the main commodities traded in the world. Brazil is the largest producer and exporter of the grain. Fungal diseases are very common in coffee crops and control represents a large part of coffee production costs. The climate is an essential factor in the development of a disease such as phoma leaf spot. This disease is favored by high atmospheric humidity and mild temperatures. In this context, this study aimed to carry out the climatic favorability zoning for one of the main coffee diseases (Phoma ssp.) of the coffee-growing region in Brazil. The study was conducted in the main traditional coffee growing regions, i.e., the states of Paraná (PR), São Paulo (SP), Rio de Janeiro (RJ), Espírito Santo (ES), Minas Gerais (MG), Goiás (GO), and Bahia (BA), totaling 2730 municipalities. Air temperature and daily precipitation data for the current scenario were collected from the WorldClim version 2.1 platform for the latest climatological normal in GeoTIFF format. Future climate variables were obtained by the WorldClim 2.1 platform for the IPSL-CM6A-LR global climate model for the periods 2021-2040, 2041-2060, 2061-2080, and 2081-2100 and the scenarios SSP-1 2.6, SSP-2 4.5, SSP-3 7.0, and SSP-5 8.5, respectively. Thus, zoning was carried out using software of geographic information systems (QGIS), automated with the Python language. Also, graphs were prepared to better represent the results. About 54.77% of the coffee-producing region presented relatively favorable conditions for the development of Phoma leaf spot, 30.55% favorable, 3.20% highly, and 11.48% showed no climate conditions for the occurrence of the disease. The climate conditions from October to March favored the occurrence of phoma leaf spot. The Phoma spp. Leaf spot will probably reduce its occurrence in all future scenarios due to the loss of favorable climate conditions. During the period 2081-2100, 85.03% of the entire region would be unfavorable to the development of homas pp. For the most pessimistic scenario (SSP-5 8.5). Climate changes will provide unsuitable conditions for the development of Phoma spp.

Anthropogenic and internal drivers of wind changes over the Amundsen Sea, West Antarctica, during the 20th and 21st centuries

Abstract. Ocean-driven ice loss from the West Antarctic Ice Sheet is a significant contributor to sea-level rise. Recent ocean variability in the Amundsen Sea is controlled by near-surface winds. We combine palaeoclimate reconstructions and climate model simulations to understand past and future influences on Amundsen Sea winds from anthropogenic forcing and internal climate variability. The reconstructions show strong historical wind trends. External forcing from greenhouse gases and stratospheric ozone depletion drove zonally uniform westerly wind trends centred over the deep Southern Ocean. Internally generated trends resemble a South Pacific Rossby wave train and were highly influential over the Amundsen Sea continental shelf. There was strong interannual and interdecadal variability over the Amundsen Sea, with periods of anticyclonic wind anomalies in the 1940s and 1990s, when rapid ice-sheet loss was initiated. Similar anticyclonic anomalies probably occurred prior to the 20th century but without causing the present ice loss. This suggests that ice loss may have been triggered naturally in the 1940s but failed to recover subsequently due to the increasing importance of anthropogenic forcing from greenhouse gases (since the 1960s) and ozone depletion (since the 1980s). Future projections also feature strong wind trends. Emissions mitigation influences wind trends over the deep Southern Ocean but has less influence on winds over the Amundsen Sea shelf, where internal variability creates a large and irreducible uncertainty. This suggests that strong emissions mitigation is needed to minimise ice loss this century but that the uncontrollable future influence of internal climate variability could be equally important.

Environmental drivers of demography and potential factors limiting the recovery of an endangered marine top predator

AbstractUnderstanding what drives changes in wildlife demography is fundamental to the conservation and management of depleted or declining populations, though making inference about the intrinsic and extrinsic factors that influence survival and reproduction remains challenging. Here we use mark–resight data from 2000 to 2018 to examine the effects of environmental variability on age‐specific survival and natality for the endangered western distinct population segment (wDPS) of Steller sea lions (Eumetopias jubatus) in Alaska, USA. Though this population has been studied extensively over the last four decades, the causes of divergent abundance trends that have been observed across the wDPS range remain unknown. We developed a Bayesian multievent mark–resight model that accounts for female reproductive state uncertainty. Annual survival probabilities for male pups (0.44; 0.36–0.53), female yearlings (0.63; 0.49–0.73), and male yearlings (0.62; 0.51–0.71) born in the western portion of the wDPS range, estimated here for the first time, were lower than those in the eastern portion of the wDPS range, estimated as: male pups (0.69; 0.65–0.74), female yearlings (0.76; 0.71–0.81), and male yearlings (0.71; 0.65–0.78). There was a higher proportion of young female breeders in the western portion of the range, but overall natality was lower (0.69; 0.47–0.96) than in the eastern portion of the range (0.80; 0.74–0.84). Additionally, pup mass had a positive effect on pup survival in the eastern portion of the range and a negative effect in the western portion of the range, potentially due to earlier weaning of heavier pups. Local‐ and basin‐scale oceanographic features such as the Aleutian Low, the Arctic Oscillation Index, the North Pacific Gyre Oscillation, chlorophyll concentration, upwelling, and wind in certain seasons were correlated with vital rates. However, drawing strong inferences from these correlations is challenging given that relationships between ocean conditions and an adaptive top predator in a dynamic ecosystem are exceedingly complex. This study provides the first demographic rate estimates for the western portion of the range where abundance estimates continue to decline. These results will advance efforts to identify factors driving regionally divergent abundance trends, with implications for population‐level responses to future climate variability.

Climate variability impacts on runoff projection under quantile mapping bias correction in the support CMIP6: An investigation in Lushi basin of China

In this study, we modelled the XAJ (Xin’anjiang) hydrologic model parameters for the Lushi basin of China, an important tributary of the Yellow River. Time series of daily precipitation, evaporation, and runoff data from 1976 to 1995 were used to calibrate the model, while the data from 1996 to 2000 were used for validation. Thereafter, the future climate variability was projected from 2021 to 2100 for 0.22° grids under CMIP6 CORDEX-East-Asia (Coordinated Regional Climate Downscaling Experiment-East-Asia) RCP2.6 and RCP8.5 climate scenarios. The non-parametric quantile mapping (QM) bias correction method was used to minimize the large differences between climate variability directly derived from the regional circulation model (RCM) and the historical observations, and the results were also compared with the baseline period (1976–2000). Overall, when compared to the baseline case, results showed a decline in the annual precipitation by 7.22 % and 5.01 %, while an increase was observed for the annual evaporation by 2.03 % and 3.58 % under RCP2.6 and RCP8.5 climate scenarios in the 21st century. Annual runoff projection by XAJ model indicate reduction, with 9.3 % and 31.2 % fall for short-term (2021–2040), 31.5 % and 41.2 % fall for mid-term (2051–2070) and 32.3 % and 77.4 % fall for long-term (2081–2100), respectively. The extreme streamflow projects increase during the dry period (November to June) with 76.5 m3/s and 84.6 m3/s under RCP2.6 and RCP8.5 compared to 45.6 m3/s from the baseline period. In contrast, there is a decrease during the wet period (July to October) by 390 m3/s and 237 m3/s compared to 405 m3/s in the baseline data of the same period. Such projections become more pronounced toward the end of the 21st century, and the reduction is likely to continue if future climate projections occur. The hydrological parameters can be applied to adjacent and similar landscape basins for disaster forecasting and early warning. The QM method is effective in bias correction. The findings can also be used for flood and drought mitigation planning and water resources management in the Yellow River Basin.

Investigating the Effects of Climate Change on Material Properties and Structural Performance

One major issue when considering the effects of climate change is to understand, qualify and quantify how the changing climate will likely impact infrastructure assets and services as it strongly depends on current and future climate variability, location, asset design life, function and condition. Expected changes to local climatic conditions may potentially lead to changes in the degradation processes of building materials, affecting the durability and service life of structures. A key question is how climate change may produce changes of vulnerability owing to physical and chemical actions affecting structural durability or changes to exposure in terms of the intensity/frequency of extreme weather events. In this context, this article focuses on the impact on structural resistance and associated challenges, considering some recent activities of members of IABSE TG6.1. Several case studies of infrastructure assets worldwide are presented and discussed in this article.

Investigating the Effects of Climate Change on Structural Actions

The changing climate with resulting more extreme weather events will likely impact infrastructure assets and services. This phenomenon can present direct threats to the assets as well as significant indirect effects for those relying on the services those assets deliver. Such threats are path-dependent and place-specific, as they strongly depend on current and future climate variability, location, asset design life, function and condition. One key question is how climate change is likely to increase both the probability and magnitude of extreme weather events under different scenarios of climate change. To address this issue, this paper investigates selected effects of climate change and their consequences on structural performance, in the context of evolving loading scenarios in three different continental regions: Europe, North America, and Asia. The aim is to investigate some main place-specific changes of the exposure in terms of intensity/frequency of extreme events as well as the associated challenges, considering some recent activities of members of the IABSE TG6.1. Climate change can significantly affect built infrastructure and the society by increasing the occurrence and magnitude of extreme events and increasing potential losses. Therefore, specific relationships relating hazard levels and structural vulnerability to climate change effects should be determined.

A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

AbstractMultimodel ensemble (MME) approach would help modellers to know the advantages of individual global circulation models (GCMs) and to avoid the weaknesses associated with them, and it would help the river basin modellers to make appropriate modelling decisions. The study highlights the river basin‐scale development of MME as a convenient way to reduce the parameter and structural uncertainties in the Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs simulations after identifying the best five CMIP6 GCMs based on the rating metric calculations. Furthermore, the performance of the MME was enhanced by integrating three machine learning algorithms (artificial neural network [ANN], random forest [RF], support vector machine [SVM]). Subsequently, comparative assessment depicted the improved performance in MME‐integrated ML algorithms compared to simple arithmetic mean (SAM) in simulating observed precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin) over the Damodar River basin (DRB), India. The statistical metrics indicate that the SVM and RF methods yielded better results than SAM and ANN methods, thus selected for future projections. The robustness of the MME‐RF and MME‐SVM approach has also been observed while capturing the spatial pattern as IMD‐observed with well representation of climate indices for both wet and dry seasons. Future projections with MME‐SVM and MME‐RF suggested a possible rise in mean annual P in the range of 1.4–15% and 6.8–39% with an increasing trend in temperature (Tmax, Tmin) under the SSP245 and SSP585 scenarios, respectively. Replicating the spatial pattern of the future climatic variables projections evinced a warmer and drier climate in the southwest part of the DRB for both SSP scenarios during wet and dry season and thence warned a probable drier condition on the southwest part of the DRB in future time slices.

Formalizing Trust in Historical Weather Data

Abstract Historical instrumental weather observations are vital to understanding past, present, and future climate variability and change. However, the quantity of historical weather observations to be rescued globally far exceeds the resources available to do the rescuing. Which observations should be prioritized? Here we formalize guidelines help make decisions on rescuing historical data. Rather than wait until resource-intensive digitization is done to assess the data’s value, insights can be gleaned from the context in which the observations were made and the history of the observers. Further insights can be gained from the transcription platforms used and the transcribers involved in the data rescue process, without which even the best historical observations can be mishandled. We use the concept of trust to help integrate and formalize the guidelines across the life cycle of data rescue, from the original observation source to the transcribed data element. Five cases of citizen science-based historical data rescue, two from Canada and three from Australia, guide us in constructing a trust checklist. The checklist assembles information from the original observers and their observations to the current transcribers and transcription approaches they use. Nineteen elements are generated to help future data rescue projects answer the question of whether resources should be devoted to rescuing historical meteorological material under consideration. Significance Statement Historical weather observations, such as ships’ logs and weather diaries, help us to understand our past, present, and future climate. More observations are waiting to be rescued than there are resources. Only after they have been rescued—transcribed—can the records be indexed, searched, and analyzed. Given the vast task, citizen scientists are often recruited to transcribe past weather records. Various tools, including software platforms, help volunteers transcribe these handwritten records. We provide guidance on choosing observations to rescue. This guidance is novel because it emphasizes trust throughout the data rescue process: trust in who the observers were and how the observations were made, trust in who the current transcribers are, and trust in the software tools that are used for transcription.

Optimal design of low-carbon energy systems towards sustainable cities under climate change scenarios

The design of low-carbon energy systems towards sustainable cities requires correctly quantifying uncertainties in future climate variations, since they affect both energy demand of urban cities and energy system performance. However, quantifying climate uncertainties is challenging due to the stochastic and unpredictable nature of future climate events. This paper develops a novel and general framework to quantify climate uncertainties for urban energy system design using scenario-based stochastic optimization. Climate models are coupled with building performance simulation to generate scenarios for uncertainty quantification and energy system optimization. We apply this optimization framework to US cities for demonstration. We find that climate uncertainties in renewable generation and energy demand can lead to significant performance differences in urban energy systems. Neglecting climate uncertainties results in an optimistic energy system; however, it is unable to provide stable power supply to urban cities. Moreover, we observe that maintaining a certain degree of grid integration is beneficial for urban energy systems and can alleviate the economic burden of urban cities. Finally, we provide some valuable insights on urban energy systems for system designer and policymakers. The designers should fully consider uncertainties associated with various climate events during system design and deploy proper backup systems in case of contingency. The policymakers should thoroughly account for climate uncertainties and their resulting costs when stipulating energy policies for promoting low-carbon energy systems. Both designers and policymakers need to cooperate together and coordinate their actions on urban energy systems towards sustainable cities.

Impacts of Future Climate Variability on Atrazine Accumulation and Transport in Corn Production Areas in the Midwestern United States.

Atrazine is one of the most prevalent herbicides that has been widely applied to agricultural lands in the U.S. Understanding the transport and accumulation of atrazine in the subsurface under future climate scenarios is essential for future agriculture and water management. Here, we predict atrazine transport and accumulation under an intensive corn production land based on 20 projected global climate model (GCM) realizations, while considering uncertainties of transport parameters. Our study predicted continuous groundwater table declination and atrazine mass accumulation on the study site. We show that atrazine mass accumulation in corn production areas is subject to total precipitation in the atrazine application season, whereas atrazine plume movement is controlled by the sequence of annual precipitation. Atrazine mass transport and accumulation are more sensitive to climate variation on the field sites with low sorption and atrazine degradation rate. Under the extreme condition, the atrazine plume can migrate as far as five meters from the ground surface in only three years. While annual mean precipitation in the Midwestern U.S. is projected to increase in the future, groundwater vulnerability to atrazine and associated water quality impacts may rise in the U.S. Corn Belt, especially in sites with low atrazine degradation and sorption.

Projected future changes in the contribution of Indo-Pacific sea surface height variability to the Indonesian throughflow

The Indonesian throughflow (ITF) transports a significant amount of warm freshwater from the Pacific to the Indian Ocean, making it critical to the global climate system. This study examines decadal ITF variations using ocean reanalysis data as well as climate model simulations from the Coupled Model Inter-comparison Project Phase 5 (CMIP5). While the observed annual cycle of ITF transport is known to be correlated with the annual cycle of sea surface height (SSH) difference between the Pacific and Indian Oceans, ocean reanalysis data (1959–2015) show that the Pacific Ocean SSH variability controls more than 85% of ITF variation on decadal timescales. In contrast, the Indian Ocean SSH variability contributes less than 15%. While those observed contributions are mostly reproduced in the CMIP5 historical simulations, an analysis of future climate projections shows a 25–30% increase in the Indian Ocean SSH variability to decadal ITF variations and a corresponding decrease in the Pacific contribution. These projected changes in the Indian Ocean SSH variability are associated with a 23% increase in the amplitudes of negative zonal wind stress anomalies over the equatorial Indian Ocean, along with a 12º eastward shift in the center of action in these anomalies. This combined effect of the increased amplitude and eastward shift in the zonal wind stress increases the SSHA variance over the Indian Ocean, increasing its contribution to the ITF variation. The decadal ITF changes discussed in this study will be crucial in understanding the future global climate variability, strongly coupled to Indo-Pacific interactions.

Spatio–temporal variation of vegetation heterogeneity in groundwater dependent ecosystems within arid environments

Climate change, land cover change and the over–abstraction of groundwater threaten the existence of Groundwater-Dependent Ecosystems (GDE), despite these environments being regarded as biodiversity hotspots. The vegetation heterogeneity in GDEs requires routine monitoring in order to conserve and preserve the ecosystem services in these environments. However, in–situ monitoring of vegetation heterogeneity in extensive, or transboundary, groundwater resources remain a challenge. Inherently, the Spectral Variation Hypothesis (SVH) and remotely-sensed data provide a unique way to monitor the response of GDEs to seasonal or intra–annual environmental stressors, which is the key for achieving the national and regional biodiversity targets. This study presents the first attempt at monitoring the intra–annual, spatio–temporal variations in vegetation heterogeneity in the Khakea–Bray Transboundary Aquifer, which is located between Botswana and South Africa, by using the coefficient of variation derived from the Landsat 8 OLI Operational Land Imager (OLI). The coefficient of variation was used to measure spectral heterogeneity, which is a function of environmental heterogeneity. Heterogenous environments are more diverse, compared to homogenous environments, and the vegetation heterogeneity can be inferred from the heterogeneity of a landscape. The coefficient of variation was used to calculate the α- and β measures of vegetation heterogeneity (the Shannon–Weiner Index and the Rao's Q, respectively), whilst the monotonic trends in the spatio–temporal variation (January–December) of vegetation heterogeneity were derived by using the Mann–Kendall non–parametric test. Lastly, to explain the spatio–temporal variations of vegetation heterogeneity, a set of environmental variables were used, along with a machine-learning algorithm (random forest). The vegetation heterogeneity was observed to be relatively high during the wet season and low during the dry season, and these changes were mainly driven by landcover- and climate–related variables. More specifically, significant changes in vegetation heterogeneity were observed around natural water pans, along roads and rivers, as well as in cropping areas. Furthermore, these changes were better predicted by the Rao's Q (MAE = 5.81, RMSE = 6.63 and %RMSE = 42.41), than by the Shannon–Weiner Index (MAE = 30.37, RMSE = 33.25 and %RMSE = 63.94). These observations on the drivers and changes in vegetation heterogeneity provide new insights into the possible effects of future landcover changes and climate variability on GDEs. This information is imperative, considering that these environments are biodiversity hotspots that are capable of supporting many livelihoods. More importantly, this work provides a spatially explicit framework on how GDEs can be monitored to achieve Sustainable Development Goal (SDG) Number 15.

Potential risks of climate variability on rice cultivation regions in the Mekong Delta, Vietnam

ABSTRACT In recent decades, the rice cultivation regions in the Mekong Delta have continuously suffered from unprecedented weather events due to a decline in rainfall as part of climate variability. The aim of this study was to perform a comprehensive exploration of the rainfall characteristics across the area, applying the Rainfall Anomaly Index (RAI), Spearman Rho test and Sen slope estimator to help track the weather as well as provide warnings on the potential risks caused by alterations in rainfall amounts. For this goal, the rainfall data sequences at 14 national observation stations across the Mekong Delta were collected for the 1984 - 2019 period. Results indicated that the dry weather seasons occurred more frequently during the normally wet weather seasons. Four typical dry weather seasons were identified for the 1997 - 1998, 2002 - 2004, 2014 - 2016, and 2018 - 2019 periods. Among these, the 2014 - 2016 period was the driest, with 9 out of 12 stations in the area being extremely dry and RAI risk peaks as high as -4.86 at the Moc Hoa station in the province of Long An. A weather trend of decreasing rainfall was evident, mainly in the coastal sub-regions. The discovery of changing rainfall trends is valuable for predicting future climate variability.

Evaluating seasonal sea-ice cover over the Southern Ocean at the Last Glacial Maximum

Abstract. Southern hemispheric sea-ice impacts ocean circulation and the carbon exchange between the atmosphere and the ocean. Sea-ice is therefore one of the key processes in past and future climate change and variability. As climate models are the only tool available to project future climate change, it is important to assess their performance against observations for a range of different climate states. The Last Glacial Maximum (LGM, ∼21 000 years ago) represents an interesting target as it is a relatively well-documented period with climatic conditions very different from preindustrial conditions. Here, we analyze the LGM seasonal Southern Ocean sea-ice cover as simulated in numerical simulations as part of the Paleoclimate Modelling Intercomparison Project (PMIP) phases 3 and 4. We compare the model outputs to a recently updated compilation of LGM seasonal Southern Ocean sea-ice cover and summer sea surface temperature (SST) to assess the most likely LGM Southern Ocean state. Simulations and paleo-proxy records suggest a fairly well-constrained glacial winter sea-ice edge between 50.5 and 51∘ S. However, the spread in simulated glacial summer sea-ice is wide, ranging from almost ice-free conditions to a sea-ice edge reaching 53∘ S. Combining model outputs and proxy data, we estimate a likely LGM summer sea-ice edge between 61 and 62∘ S and a mean summer sea-ice extent of 14–15×106 km2, which is ∼20 %–30 % larger than previous estimates. These estimates point to a higher seasonality of southern hemispheric sea-ice during the LGM than today. We also analyze the main processes defining the summer sea-ice edge within each of the models. We find that summer sea-ice cover is mainly defined by thermodynamic effects in some models, while the sea-ice edge is defined by the position of Southern Ocean upwelling in others. For models included in both PMIP3 and PMIP4, this thermodynamic or dynamic control on sea-ice is consistent across both experiments. Finally, we find that the impact of changes in large-scale ocean circulation on summer sea-ice within a single model is smaller than the natural range of summer sea-ice cover across the models considered here. This indicates that care must be taken when using a single model to reconstruct past climate regimes.

Coupling genetic structure analysis and ecological-niche modeling in Kersting\u2019s groundnut in West Africa

Orphan legume crops play an important role in smallholder farmers’ food systems. Though less documented, they have the potential to contribute to adequate nutrition in vulnerable communities. Unfortunately, data are scarce about the potential of those crops to withstand current and future climate variations. Using Macrotyloma geocarpum as an example, we used ecological niche modeling to explore the role of ecology on the current and future distributions of genetic populations of Kersting’s groundnut. Our findings showed that: (1) the models had good predictive power, indicating that M. geocarpum’s distribution was correlated with both climatic and soil layers; (2) identity and similarity tests revealed that the two genetic groups have identical and similar environmental niches; (3) by integrating the genetic information in niche modeling, niches projections show divergence in the response of the species and genetic populations to ongoing climate change. This study highlights the importance of incorporating genetic data into Ecological Niche Modeling (ENM) approaches to obtain a finer information of species’ future distribution, and explores the implications for agricultural adaptation, with a particular focus on identifying priority actions in orphan crops conservation and breeding.

Climate, Environment and Socio-Economic Drivers of Global Agricultural Productivity Growth

Growth in total factor productivity (TFP) indicates the sustainable and/or judicious use of scarce resources, including non-renewables. This paper identifies sources of growth in global agricultural TFP and its finer components, ranging from climate, production environment, and socio-economic factors, using a panel data of 104 countries, covering a 45-year period (1969–2013); and, finally, projects changes in TFP from increased climate variability. The results revealed that global agricultural productivity grew consistently at a rate of 0.44% p.a., driven by technological progress and mix-efficiency change, with negligible contributions from technical- and scale-efficiency changes; albeit with variations across regions. Both long-term and short-term climatic factors and the natural production environment significantly reduce global agricultural productivity, whereas a host of socio-economic factors have a significant but varied influence. The projected increased level of future climate variability will significantly reduce future agricultural productivity. Policy implications include investments in crop diversification, education, agricultural spending, number of researchers, and country specific R&D.

Prioritizing climate change adaptation needs for hydropower sector in China

Differentiating spatial–temporal hydropower risk triggered by climate change is crucial to climate adaptation and hydropower programming. In this research, we use a fixed-effect model on 5082 plants in China to estimate how the revenue of hydropower plants responded to climate change over 16 years, and project the revenue change and fit the damage function driven by 42 climate realizations. Results show that the revenue change of the hydropower sector demonstrates substantial regional variation and would reduce by 9.34% ± 1.21% (mean ± s.d.) yr−1 on average under RCP 8.5 by 2090s as compared to 2013, about four times larger than that under RCP4.5. Carbon leakage caused by thermal power substitution reaches 467.56 ± 202.63 (112.49 ± 227.45) Mt CO2e under RCP8.5 (RCP4.5). Different climatic conditions manifest locally, and different climate resilience makes the response function regionally heterogeneous. Southwest China is identified as the priority region for adaptation through integrated evaluation of historical climate sensitivity, future climate variability, and regional hydropower importance, informing more adaptation and investment needs of further hydropower development in the area.