scholarly journals Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models

2021 ◽  
Author(s):  
Mubashra Yasin ◽  
Ashfaq Ahmad ◽  
Tasneem Khaliq ◽  
Muhammad Habib-ur-Rahman ◽  
Salma Niaz ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Growth Parameters ◽  
General Circulation Models ◽  
High Yield ◽  
Maize Crop ◽  
Maize Production ◽  
Area Index ◽  
Sowing Dates

AbstractFuture climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer’s fields for ten sowing dates during two consecutive growing years. Data about phenology, morphology, biomass development, and yield were recorded by adopting standard procedures and protocols. The CSM-CERES, APSIM, and CSM-IXIM-Maize models were calibrated and evaluated. Five GCMs among 29 were selected based on classification into different groups and uncertainty to predict climatic changes in the future. The results predicted that there would be a rise in temperature (1.57–3.29 °C) during the maize growing season in five General Circulation Models (GCMs) by using RCP 8.5 scenarios for the mid-century (2040–2069) as compared with the baseline (1980–2015). The CERES-Maize and APSIM-Maize model showed lower root mean square error values (2.78 and 5.41), higher d-index (0.85 and 0.87) along reliable R2 (0.89 and 0.89), respectively for days to anthesis and maturity, while the CSM-IXIM-Maize model performed well for growth parameters (leaf area index, total dry matter) and yield with reasonably good statistical indices. The CSM-IXIM-Maize model performed well for all hybrids during both years whereas climate models, NorESM1-M and IPSL-CM5A-MR, showed less uncertain results for climate change impacts. Maize models along GCMs predicted a reduction in yield (8–55%) than baseline. Maize crop may face a high yield decline that could be overcome by modifying the sowing dates and fertilizer (fertigation) and heat and drought-tolerant hybrids.

Ensemble climate predictions using climate models and observational constraints

2007 ◽  
Vol 365 (1857) ◽  
pp. 2029-2052 ◽  
Author(s):  
Peter A Stott ◽  
Chris E Forest
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Changes ◽  
Climate Models ◽  
State Of The Art ◽  
General Circulation Models ◽  
Computationally Efficient ◽  
Temperature Changes ◽  
Large Ensembles ◽  
Past Climate Change

Two different approaches are described for constraining climate predictions based on observations of past climate change. The first uses large ensembles of simulations from computationally efficient models and the second uses small ensembles from state-of-the-art coupled ocean–atmosphere general circulation models. Each approach is described and the advantages of each are discussed. When compared, the two approaches are shown to give consistent ranges for future temperature changes. The consistency of these results, when obtained using independent techniques, demonstrates that past observed climate changes provide robust constraints on probable future climate changes. Such probabilistic predictions are useful for communities seeking to adapt to future change as well as providing important information for devising strategies for mitigating climate change.

scholarly journals Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: the case study of Latyan watershed

2018 ◽  
Vol 11 (1) ◽  
pp. 200-216 ◽  
Author(s):  
Reza Haji Hosseini ◽  
Saeed Golian ◽  
Jafar Yazdi
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Fuzzy Inference ◽  
Global Climate ◽  
General Circulation Models ◽  
Global Climate Models ◽  
Data Driven ◽  
Observation Data ◽  
Inference System

Abstract Assessment of climate change in future periods is considered necessary, especially with regard to probable changes to water resources. One of the methods for estimating climate change is the use of the simulation outputs of general circulation models (GCMs). However, due to the low resolution of these models, they are not applicable to regional and local studies and downscaling methods should be applied. The purpose of the present study was to use GCM models' outputs for downscaling precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the observation data from the Amameh station during the 1980–2005 period, 26 output variables from two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS). Comparison of the monthly results showed the superiority of KNN compared to the other two methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the study area.

Interpreting Results from the NARCCAP and NA-CORDEX Ensembles in the Context of Uncertainty in Regional Climate Change Projections

2018 ◽  
Vol 99 (10) ◽  
pp. 2093-2106 ◽  
Author(s):  
Ambarish V. Karmalkar
Keyword(s):  
Climate Change ◽  
North America ◽  
General Circulation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Internal Variability ◽  
General Circulation Models ◽  
Eastern United States ◽  
The Impact

AbstractTwo ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results.

scholarly journals Global Circulation Models (GCMs) Simulate the Current Temperature Only If the Shortwave Radiation Anomaly of the 2000s Has Been Omitted

2021 ◽  
pp. 45-52
Author(s):  
Antero Ollila
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Coupled Model ◽  
General Circulation Models ◽  
Scientific Basis ◽  
Shortwave Radiation ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Paris Climate Agreement

The research article of Gillett et al. was published in Nature Climate Change (NCC) in March 2021. The objective of the NCC study was to simulate human-induced forcings to warming by applying 13 CMIP6 (Coupled Model Intercomparison Project Phase 6) climate models. NCC did not accept the author’s remarks as a “Matters arising” article. The purpose of this article is to detail the original three remarks and one additional remark: 1) the discrepancy between the graphs and reported numerical values, 2) the forcings of aerosols and clouds, 3) the positive water feedback, and 4) the calculation basis of the Paris agreement. The most important finding is that General Circulation Models (GCMs) used in simulations omit the significant shortwave anomaly from 2001 to 2019, which causes a temperature error of 0.3°C according to climate change physics of Gillett et al. For the year 2019, this error is 0.8°C showing the magnitude of shortwave anomaly impact. The main reason for this error turns out to be the positive water feedback generally applied in climate models. The scientific basis of the Paris climate agreement is faulty for the same reason.

Current and potential future distributions of Hass avocados in the face of climate change across the Americas

2019 ◽  
Vol 70 (8) ◽  
pp. 694 ◽  
Author(s):  
Joaquín Guillermo Ramírez-Gil ◽  
Marlon E. Cobos ◽  
Daniel Jiménez-García ◽  
Juan Gonzalo Morales-Osorio ◽  
A. Townsend Peterson
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Potential Distribution ◽  
Climate Models ◽  
General Circulation Models ◽  
Persea Americana ◽  
Future Projections ◽  
The Face ◽  
Emissions Scenarios ◽  
Hass Avocado

Climate change is a global phenomenon that presents diverse threats to global food security. Of the avocados (Persea americana Mill), Hass is the most commonly cultivated variety in the world, representing an important source of nutrition in numerous countries, yet its potential risks in the face of climate change are unknown. Here, we characterise current and future potential distributional areas for Hass avocado under different scenarios of climate change across the Americas. We use ecological-niche modelling approaches to explore implications of changes in climate, considering 22 general circulation models, two emissions scenarios, and six model parameterisations. The current potential distribution of Hass avocado extends across tropical America (excluding most of Amazonia), including some areas at higher latitudes. Future projections show stability in potential distribution. Range expansions are expected mainly in temperate areas, and range contractions are related to temperature and precipitation increases, mostly in Amazonia. Model parametrisations contributed the most to overall variation in future projections, followed by climate models, and then emissions scenarios. Our conclusion of relative stability for the crop’s potential distribution is still subject to effects on other components of avocado production systems, and may be vulnerable to extreme phenomena.

Applying the new spatially distributed Added Value Index and Climate Change Downscaling Signal for Regional Climate Models to high-resolution EURO-CORDEX and convection permitting scale simulations

2021 ◽  
Author(s):  
James Ciarlo ◽  
Erika Coppola ◽  
Emanuela Pichelli ◽  
Jose Abraham Torres Alavez ◽  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
General Circulation ◽  
Climate Models ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Regional Climate Models ◽  
General Circulation Models ◽  
Added Value ◽  
Spatially Distributed

<p>Downscaling data from General Circulation Models (GCMs) with Regional Climate Models (RCMs) is a computationally expensive process, even more so running at the convection permitting scale (CP). Despite the high-resolution products of these simulations, the Added Value (AV) of these runs compared to their driving models is an important factor for consideration. A new method was recently developed to quantify the AV of historical simulations as well as the Climate Change Downscaling Signal (CCDS) of forecast runs. This method presents these quantities spatially and thus the specific regions with the most AV can be identified and understood.</p><p>An analysis of daily precipitation from a 55-model EURO-CORDEX ensemble (at 12 km resolution) was assessed using this method. It revealed positive AV throughout the domain with greater emphasis in regions of complex topography, coast-lines, and the tropics. Similar CCDS was obtained when assessing the RCP 8.5 far future runs in these domains. This paper looks more closely at the CCDS obtained with this method and compares it to other climate change signals described in other studies.</p><p>The same method is now being applied to assess the AV and CCDS of daily precipitation from an ensemble of models at the CP scale (~3 km) over different domains within Europe. The current stage of the analysis is also looking into the AV of using hourly precipitation instead of daily.</p>

Implications of climate change for water resources development in the Ganges basin

Water Policy ◽  
2013 ◽  
Vol 15 (S1) ◽  
pp. 26-50 ◽  
Author(s):  
Marc Jeuland ◽  
Nagaraja Harshadeep ◽  
Jorge Escurra ◽  
Don Blackmore ◽  
Claudia Sadoff
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
General Circulation Models ◽  
Low Flow ◽  
Climate Projections ◽  
Intergovernmental Panel ◽  
Gangetic Plain ◽  
Hydropower Potential ◽  
The Ganges

This paper presents the first basin-wide assessment of the potential impact of climate change on the hydrology and production of the Ganges system, undertaken as part of the World Bank's Ganges Strategic Basin Assessment. A series of modeling efforts – downscaling of climate projections, water balance calculations, hydrological simulation and economic optimization – inform the assessment. We find that projections of precipitation across the basin, obtained from 16 Intergovernmental Panel on Climate Change-recognized General Circulation Models are highly variable, and lead to considerable differences in predictions of mean flows in the main stem of the Ganges and its tributaries. Despite uncertainties in predicted future flows, they are not, however, outside the range of natural variability in this basin, except perhaps at the tributary or sub-catchment levels. We also find that the hydropower potential associated with a set of 23 large dams in Nepal remains high across climate models, largely because annual flow in the tributary rivers greatly exceeds the storage capacities of these projects even in dry scenarios. The additional storage and smoothing of flows provided by these infrastructures translates into enhanced water availability in the dry season, but the relative value of this water for the purposes of irrigation in the Gangetic plain, and for low flow augmentation to Bangladesh under climate change, is unclear.

scholarly journals Evaluating the effects of climate change on precipitation and temperature for Iran using RCP scenarios

2020 ◽  
Author(s):  
Shahab Doulabian ◽  
Saeed Golian ◽  
Amirhossein Shadmehri Toosi ◽  
Conor Murphy
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Surface Air Temperature ◽  
General Circulation Models ◽  
Skill Score ◽  
Climatic Conditions ◽  
Historical Period ◽  
Rcp Scenarios ◽  
Wide Range

Abstract Climate change has caused many changes in hydrologic processes and climatic conditions globally, while extreme events are likely to occur more frequently at a global scale with continued warming. Given the importance of general circulation models (GCMs) as an essential tool for climate studies at global/regional scales, together with the wide range of GCMs available, selecting appropriate models is of great importance. In this study, six synoptic weather stations were selected as representative of different climatic zones over Iran. Utilizing monthly data for 20 years (1981–2000), the outputs of 25 GCMs for surface air temperature (SAT) and precipitation were evaluated for the historical period. The root-mean-square error and skill score were chosen to evaluate the performance of GCMs in capturing observed seasonal climate. Finally, the outputs of selected GCMs for the three Representative Concentration Pathways emission scenarios (RCPs), namely RCP2.6, RCP4.5, and RCP8.5, were downscaled using the change factor method for each station for the period 2046–2065. Results indicate that SAT in all months is likely to increase for each region, while for precipitation, large uncertainties emerge, despite the selection of climate models that best capture the observed seasonal cycle. These results highlight the importance of selecting a representative ensemble of GCMs for assessing future hydro-climatic changes for Iran.

Harvest disruption projections for the Australian sugar industry

2015 ◽  
Vol 7 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Justin Sexton ◽  
Yvette Everingham ◽  
Bertrand Timbal
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Sugar Industry ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Content Type ◽  
Novel Approach ◽  
Central Regions ◽  
Australian Coastline

Purpose – This study aims to investigate the effects of climate change on harvestability for sugarcane-growing regions situated between mountain ranges and the narrow east Australian coastline. Design/methodology/approach – Daily rainfall simulations from 11 general circulation models (GCMs) were downscaled for seven Australian sugarcane regions (1961:2000). Unharvestable days were calculated from these 11 GCMs and compared to interpolated observed data. The historical downscaled GCM simulations were then compared to simulations under low (B1) and high (A2) emissions scenarios for the period of 2046-2065. The 25th, 50th and 75th percentiles of paired model differences were assessed using 95 per cent bootstrapped confidence intervals. Findings – A decrease in the number of unharvestable days for the Burdekin (winter/spring) and Bundaberg (winter) regions and an increase for the Herbert region (spring) were plausible under the A2 scenario. Spatial plots identified variability within regions. Northern and southern regions were more variable than central regions. Practical implications – Changes to the frequency of unharvestable days may require a range of management adaptations such as modifying the harvest period and upgrading harvesting technologies. Originality/value – The application of a targeted industry rainfall parameter (unharvestable days) obtained from downscaled climate models provided a novel approach to investigate the impacts of climate change. This research forms a baseline for industry discussion and adaptation planning towards an environmentally and economically sustainable future. The methodology outlined can easily be extended to other primary industries impacted by wet weather.

Characterizing the uncertainty of climate change projections using hierarchical models

2018 ◽  
Author(s):  
Carlos Carvalho ◽  
Jill Rickershauser
Keyword(s):  
Climate Change ◽  
Hierarchical Models ◽  
General Circulation ◽  
Climate Models ◽  
General Circulation Models ◽  
Climate Change Projections ◽  
Bayesian Hierarchical ◽  
Current State ◽  
Modelling Assumptions ◽  
Precipitation Changes

This article focuses on the use of Bayesian hierarchical models for integration and comparison of predictions from multiple models and groups, and more specifically for characterizing the uncertainty of climate change projections. It begins with a discussion of the current state and future scenarios concerning climate change and human influences, as well as various models used in climate simulations and the goals and challenges of analysing ensembles of opportunity. It then introduces a suite of statistical models that incorporate output from an ensemble of climate models, referred to as general circulation models (GCMs), with the aim of reconciling different future projections of climate change while characterizing their uncertainty in a rigorous fashion. Posterior distributions of future temperature and/or precipitation changes at regional scales are obtained, accounting for many peculiar data characteristics. The article confirms the reasonableness of the Bayesian modelling assumptions for climate change projections' uncertainty analysis.

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