Climate impacts and adaptation studies often use output from impact models that require data representing future climates at a resolution greater than can be provided by Global Climate Models (GCMs). ...This paper describes the use of Regional Climate Model (RCM) simulations to generate high-resolution future climate information for assessing climate impacts in the Ganges-Brahmaputra-Meghna (GBM) and Mahanadi deltas as part of the DECCMA project. In this study, three different GCMs (HadGEM2-ES, CNRM-CM5 and GFDL-CM3), all using a single scenario for future greenhouse forcing of the atmosphere (RCP 8.5), were downscaled to a horizontal resolution of 25 km over south Asia using the HadRM3P RCM. These three GCMs were selected based on ability to represent key climate processes over south Asia and ability to sample a range of regional climate change responses to greenhouse gas forcing. RCM simulations of temperature, precipitation, and lower level (850 hPa) atmospheric circulation in the monsoon season (June, July, August, September – JJAS) were compared with observational datasets and their respective driving GCMs to ensure large-scale consistency. Although there are some biases in the RCM simulations, these comparisons indicate that the RCMs are able to simulate realistically aspects of the observed climate of South Asia, such as the monsoon circulation and associated precipitation that are key for informing downstream impacts and adaptation studies. Simulated future temperature and precipitation changes on seasonal and daily timescales suggest increases in both temperature and precipitation across all three models during the monsoon season, with an increase in the amount of extremely heavy precipitation over the GBM and Mahanadi basins. Despite different driving conditions, these results are consistent across all three RCM simulations, providing a level of confidence in the magnitudes and spatial characteristics of temperature and precipitation projections for South Asia.
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•Successfully generated a novel, high-resolution climate dataset over South Asia.•This dataset is fit for purpose in downstream impacts modelling and adaptation work.•Results suggest an increase in temperature, monsoon strength and rainfall.•RCMs suggest increased risk of widespread flooding during the monsoon season.
To better anticipate potential impacts of climate change, diverse information about the future is required, including climate, society and economy, and adaptation and mitigation. To address this ...need, a global RCP (Representative Concentration Pathways), SSP (Shared Socio-economic Pathways), and SPA (Shared climate Policy Assumptions) (RCP–SSP–SPA) scenario framework has been developed by the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC-AR5). Application of this full global framework at sub-national scales introduces two key challenges: added complexity in capturing the multiple dimensions of change, and issues of scale. Perhaps for this reason, there are few such applications of this new framework. Here, we present an integrated multi-scale hybrid scenario approach that combines both expert-based and participatory methods. The framework has been developed and applied within the DECCMA11DECCMA (DEltas, vulnerability and Climate Change: Migration and Adaptation) project is part of the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA), with financial support from the UK Government's Department for International Development (DFID) and the International Development Research Centre (IDRC), Canada. For more information, visit the project website: http://www.geodata.soton.ac.uk/deccma/. project with the purpose of exploring migration and adaptation in three deltas across West Africa and South Asia: (i) the Volta delta (Ghana), (ii) the Mahanadi delta (India), and (iii) the Ganges-Brahmaputra-Meghna (GBM) delta (Bangladesh/India). Using a climate scenario that encompasses a wide range of impacts (RCP8.5) combined with three SSP-based socio-economic scenarios (SSP2, SSP3, SSP5), we generate highly divergent and challenging scenario contexts across multiple scales against which robustness of the human and natural systems within the deltas are tested. In addition, we consider four distinct adaptation policy trajectories: Minimum intervention, Economic capacity expansion, System efficiency enhancement, and System restructuring, which describe alternative future bundles of adaptation actions/measures under different socio-economic trajectories. The paper highlights the importance of multi-scale (combined top-down and bottom-up) and participatory (joint expert-stakeholder) scenario methods for addressing uncertainty in adaptation decision-making. The framework facilitates improved integrated assessments of the potential impacts and plausible adaptation policy choices (including migration) under uncertain future changing conditions. The concept, methods, and processes presented are transferable to other sub-national socio-ecological settings with multi-scale challenges.
An integrated scenario framework for applying the global RCP–SSP–SPA scenario framework at sub-national scale: A multi-scale and participatory approach. The generic framework (left) is applied and demonstrated within the DECCMA project to explore migration and adaptation in deltas (right, showing the various scales of interest and broad workflow). Display omitted
•We develop a framework for applying the global RCP-SSP-SPA scenario framework at sub-national scales.•The framework is applied and tested for deltas to explore migration and adaptation.•We demonstrated the benefits of a multi-dimensional approach to capture different drivers of change.•Highlighted the need to integrate the best science and stakeholder views.•The concept, methods, processes are transferrable to other sub-national settings with multi-scale challenges.
Our changing climate is already affecting the transmission of dengue fever, the fastest growing vector-borne viral disease in the world. This issue presents a significant public health concern for ...some nations, such as Bangladesh, which already experience regular seasonal outbreaks of dengue fever under present day conditions. To provide guidance for proactive public health planning to potentially mitigate the severity of future outbreaks, we explored the impact of climate change on dengue infections by calculating the change in vectorial capacity (VC) of Aedes aegypti mosquitoes at a seasonal level for all regions in Bangladesh under two scenarios for future atmospheric greenhouse gas concentrations. For each of the four climate models used, and for both scenarios, our analysis revealed that the annual VC remains at a level that would enable potential dengue epidemic transmission in all regions in Bangladesh. We found a slight decline in VC in half of the regions examined during the last two decades of the 21st century for the lower-concentration scenario, with a pronounced decline in VC in all geographic regions beginning in 2060 for the higher-concentration scenario. The likely reason is that in many regions, warming will lead to sub-optimal mosquito breeding temperatures. However, seasonal differences in VC will dissipate as the climate warms, to the point that there is almost no observable seasonality for the higher-concentration scenario during the last two decades of this century. This finding suggests the dengue transmission season could eventually extend to all-year-round transmission, with outbreaks occurring at any time. Consequently, disease surveillance and control activities would need to be geographically and temporally adapted to mitigate dengue epidemic risk in response to climate change.
To help meet increasing demands for high‐resolution climate change projections in the Philippines, this study provides the results of multiple dynamically downscaled climate model simulations for ...projected changes in rainfall and temperature over the country by the mid‐21st century (2036–2065) relative to the baseline period (1971–2000), under the RCP8.5 scenario. The model‐simulated seasonal means of temperature, rainfall, and low‐level wind patterns were first compared with observations during the baseline period. Comparisons made between the model‐derived and APHRODITE observation‐based gridded temperature and rainfall data indicate that the dynamically downscaled simulations provide an overall improvement from their driving global climate models in capturing the spatial patterns of rainfall over the country, and the spatial and temporal characteristics of the country's mean temperature. Future climate projections show that the country's climate is expected to become warmer by the mid‐21st century, with a multi‐model ensemble mean increase of 1.2 to 1.9°C, relative to the baseline period, projected for many parts of the country and across most seasons. Slightly higher increases are projected during the country's hottest season, March–April–May. However, there are large differences in the models' projected rainfall changes by the mid‐21st century across seasons and regions. For most parts of the country, the multi‐model ensemble includes simulations that show increases and simulations that show decreases in rainfall. Nevertheless, there is a tendency of model projections towards wetter conditions over northern and central sections of the country (particularly in the December–January–February season) and drier conditions in the southern region of the country in almost all seasons. The results demonstrate the need for communities in the Philippines to adapt to a future warmer climate and prepare for a range of possible future changes in rainfall and temperature.
The climate processes involved in an archipelagic country like the Philippines, with complex topography, is unlikely to be well‐represented in the GCMs. Dynamical downscaling helps provide process‐based representations of the interactions between local topography and the atmosphere at finer spatial scales. Here, we demonstrate the improvements brought by dynamical downscaling as similarly shown in the figure. Such better representation of the country's climate increased our confidence in providing high‐resolution future climate change projections for the country.
The reliability of the enhanced greenhouse regional response of global climate models is often assessed by comparing their current climate simulation against observations. To evaluate this approach, ...we investigate the relationship between inter‐model similarity in patterns of current regional climate and inter‐model similarity in patterns of regional enhanced greenhouse response using data from a multi‐model database. Correlations of moderate magnitude are common, indicating the value of testing the current regional climate simulation of models. Notably, relationships vary significantly regionally (e.g., are weakest in the tropics), cross variables (e.g., current climate mean sea level pressure is related to temperature and precipitation change in some extra‐tropical regions) and can be insensitive to whether the current climate is assessed in the region concerned or globally. The concept demonstrated here can provide a basis for evaluating and weighting models for use in preparing projections of regional climate change.
Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) ...influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.
Delta systems formed by the deposition of sediments at the mouths of large catchments are vulnerable to sea level rise and other climate change impacts. Deltas often have some of the highest ...population densities in the world and the Mahanadi Delta in India is one of these, with a population of 39 million. The Mahanadi River is a major river in East Central India and flows through Chattisgarh and Orissa states before discharging into the Bay of Bengal. This study uses an Integrated Catchment Model (INCA) to simulate flow dynamics and water quality (nitrogen and phosphorus) and to analyze the impacts of climate change and socio-economic drivers in the Mahanadi River system. Future flows affected by large population growth, effluent discharge increases and changes in irrigation water demand from changing land uses are assessed under shared socio-economic pathways (SSPs). Model results indicate a significant increase in monsoon flows under the future climates at 2050s (2041–2060) and 2090s (2079–2098) which greatly enhances flood potential. The water availability under low flow conditions will be worsened because of increased water demand from population growth and increased irrigation in the future. Decreased concentrations of nitrogen and phosphorus are expected due to increased flow hence dilution. Socio-economic scenarios have a significant impact on water quality but less impact on the river flow. For example, higher population growth, increased sewage treatment discharges, land use change and enhanced atmospheric deposition would result in the deterioration of water quality, while the upgrade of the sewage treatment works lead to improved water quality. In summary, socio-economic scenarios would change future water quality of the Mahanadi River and alter nutrient fluxes transported into the delta region. This study has serious implications for people's livelihoods in the deltaic area and could impact coastal and Bay of Bengal water ecology.
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•Multi-branched INCA models were used to simulate flow, N and P fluxes.•Three climate models projected increases in high flow and nutrient reductions by 2090s.•Some future climate and socioeconomic scenarios lead to increases in nutrient levels.•Different socio-economic changes greatly alter nutrient fluxes transported into the delta.
•The 19 GCMs projected increasing mean temperature in the NSW wheat belt.•Spring wheat flowering date advanced across the whole region in eastern Australia.•Winter wheat flowering date delayed in the ...warmer sub-region.•The number of hot days is projected to increase while frost days decrease.
The flowering time of wheat is strongly controlled by temperature and is potentially highly sensitive to climate change. In this study, we analysed the occurrence of last frost (days with minimum temperature under 2°C) and first heat (days with maximum temperatures exceeding 30°C) events of the year to determine the optimum flowering date in the wheat belt of New South Wales (NSW), eastern Australia. We used statistically downscaled daily maximum and minimum temperature data from 19 Global Climate Models (GCMs) with a vernalizing–photothermal model in order to simulate future flowering dates and the changes in frost and hot days occurrence at flowering date (± 7 days) for two future scenarios for atmospheric greenhouse gas concentrations (RCP4.5 and RCP8.5) in 2040s (2021–2060) and 2080s (2061–2100). Relative to the 1961–2000 period, the GCMs projected increased daily maximum and minimum temperatures for these future periods, accompanied by reduced frost occurrence and increased heat stress incidence. As a consequence, by the 2080s, simulations suggest a general advance in spring wheat flowering date by, on average, 10.2 days for RCP4.5 and 17.8 days for RCP8.5 across the NSW wheat belt. Winter wheat flowering dates were delayed by an average of 2.4 days for RCP4.5 and 14.3 days for RCP8.5 in the warmest parts of the region (the northwest) due to reduced cumulative vernalization days (requiring cool conditions). In the cooler regions (the northeast, southeast and southwest), flowering date occurred earlier by 6.2 days for RCP4.5 and 6.7 days for RCP8.5 on average. Moreover, in the western parts of the wheat belt the delay of winter wheat flowering date was about 9.5 days longer than that in the eastern parts. As a result of phenological responses to increasing temperatures, current wheat varieties may not be suitable for future climate conditions, despite reduced frost risk. In the future, it may be necessary to use longer-season wheat varieties and varieties with increased heat-stress resistance.
•We analyse the impact of climate change on five crops at four locations in Australia.•Our study uses bias corrected statistically down-scaled climate data.•Field peas were more sensitive to climate ...change than other crops.•The negative impact of climate change is mainly due to advances in crop phenology.
Shifts in rainfall and rising temperatures due to climate change pose a formidable challenge to the sustainability of broadacre crop yields in Western and South-Eastern Australia. Output from18 Global Climate Models (GCMs) for the Special Report on Emission Scenarios (SRES) A2 scenario was statistically downscaled to four contrasting locations. For the first time in these regions, bias corrected statistically downscaled climate data were employed to drive the Agricultural Production Systems Simulator (APSIM) crop model that integrates the effects of soil, crop phenotype, and management options for a quantitative comparison of crop yields and phenology under an historical and a plausible projected climate. The dynamic APSIM simulation model explore the implications of climate change across multiple locations and multiple time periods (1961–2010, 2030, 2060 and 2090) for multiple key crops (wheat, barley, lupin, canola, field pea) grown in three different types of soil. On average, the ensemble of downscaled GCM projections show a decrease in rainfall in the future at the four locations considered, with increased variability at two locations. At all locations and for five crops, future changes in both crop biomass and grain yield are strongly associated with changes in rainfall (P = 0.05 to P = 0.001). The overall rainfall amount is critical in determining yields but, equally, higher future temperatures can contribute to reducing crop productivity primarily due to advanced crop phenology. For example, for wheat cropping at Hamilton (a higher rainfall site), there is a significant advancement in median flowering date for 2030, 2060, and 2090 of 10, 18, and 29 days respectively with a significant 0.50% grain yield changes for each percentage change in rainfall compared to significant 0.90% grain yield changes in Cunderdin (a lower rainfall site). At all sites except Hamilton, the change in crop grain yield is significantly correlated (P = 0.001) with the percentage change in the future rainfall and the impact increased progressively from higher rainfall to lower rainfall sites. However, the magnitude of the change in crop phenology and yield were not significantly different between soil types. These results help to define regions of concern and their relative importance in the coming years. In this future climate the negative consequences for crop yields and advancement of phenology relative to baseline are not uniform across crops and locations. Of the crops studied – wheat, barley, lupin, canola and field pea – field pea is the most sensitive to the projected future climate changes, and the ensemble median changes in field pea yield range from a decrease of 12% to a decrease of 45%, depending on location. These results highlight the importance of research and policy to support strategies for adapting to climate change, such as advances in agronomy, soil moisture conservation, seasonal climate forecasting and breeding new crop varieties.
•Effects of residue incorporation (RI) on four cropping systems is modelled under current and future climate.•Effect of RI is mainly through reduced soil evaporation and runoff.•The effect of RI is ...stronger in low-rainfall areas than high-rainfall areas.•Greater effectiveness of RI is found for cereal crops than broadleaf crops.•RI could be used for mitigation of the negative impact of climate change.
Mitigation of the deleterious impacts of climate change on agriculture is a crucial strategy for securing food resources to meet the future demand of the world with a steadily increasing population. We used a pre-validated Agricultural Production Systems sIMulator (APSIM) to explore the implementation of crop residue incorporation (RI) to mitigate the impacts of climate change on water use and crop yield for four winter crops at six sites in eastern Australia. Various residue management practices were simulated under current climate data and statistically downscaled climate data from 28 GCM simulations of RCP4.5 and RCP8.5 for the period 1900–2100. The results showed that increasing future temperature shortened crop growth duration ranged from 7.4±0.9days °C−1 for barley to 3.9±1.9days °C−1 for canola. Under projected increases in the CO2 concentration and associated climate change, the overall average crop yield for 2021–2100 in eastern Australia without RI could change by −28±5% for wheat, −22±6% for barley, −6±6% for canola and +7±17% for chickpea relative to 1951–2000 yields. With RI, crop yields could be changed by +16±14% for wheat, 11±12% for barley and 7±8% for canola and +9±17% for chickpea. Further analysis showed that greater crop transpiration was the major advantage of RI. WUE in wheat and barley also increased significantly under RI due to reduced soil evaporation and surface runoff. This effect increased under future climate changes, but the effectiveness of RI varied by location. In general, the positive effects of RI on water balance and crop yield were higher at dry sites than at wet sites. Therefore, RI can be an effective adaptation option for mitigating the impacts of climate change on winter crops by improving WUE, but is more effective in narrow-leaf cropping systems in hot and dry environments.
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