Effectiveness of using representative subsets of global climate models in future crop yield projections

Qian, Budong; Jing, Qi; Cannon, Alex J.; Smith, Ward; Grant, Brian; Semenov, Mikhail A.; Xu, Yue‐Ping; Ma, Di

doi:10.1038/s41598-021-99378-7

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…Spread-based subselection or selection with the goal of maximizing climate change signal diversity, is often carried out either alone (e.g. Semenov and Stratonovich, 2015;McSweeney and Jones, 2016;Ruane and McDermid, 2017;Qian et al, 2021), or in conjunction with performance (Lutz et al, 2016) or independence (Mendlik and Gobiet, 2016). The clear application for this approach are impact studies where worst-case scenarios are often of interest.…”

Section: As Part Of the Earth Systemmentioning

confidence: 99%

“…This allows for selected model combinations that span the cool/hot, wet/dry quadrants, as well as the "neutral" center, of the model ensemble. Qian et al (2021) further advanced spread-maximizing subselection by evaluating the T&P approach against the Katsavounidis-Kuo-Zhang (KKZ) algorithm (Katsavounidis et al, 1994), in which members are recursively selected to best span the spread of an ensemble. While both approaches had merit, the KKZ approach was more likely than the T&P approach to perform better than a randomly selected five-GCM subset in terms of both error in relation to the full-ensemble mean and coverage of the full-ensemble spread.…”

Section: As Part Of the Earth Systemmentioning

confidence: 99%

“…We assess the impact of the one family, one vote independence constraint on distributions of ECS, a key climate metric reflecting the magnitude of warming a model projects in response to CO 2 doubling from preindustrial levels (Charney et al, 1979). We source ECS values primarily from the IPCC (Smith et al, 2021), and when not available, from studies reporting to compute it via the Gregory et al ( 2004) method. Further information on the sourcing of ECS is provided in the supplement; the ECS values used are shown in Supplementary Figure S5.…”

Section: Model Families and Their Influence On Cmip Uncertaintymentioning

confidence: 99%

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Climate model Selection by Independence, Performance, and Spread (ClimSIPS) for regional applications

Merrifield

Brunner

Lorenz

et al. 2023

Preprint

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Abstract. As the number of models in Coupled Model Intercomparison Project (CMIP) archives increase from generation to generation, there is a pressing need for guidance on how to interpret and best use the abundance of newly available climate information. CMIP6 users seeking to draw conclusions about model agreement must contend with an "ensemble of opportunity" containing similar models that appear under different names. Those who used CMIP5 as a basis for downstream applications must filter through hundreds of new CMIP6 simulations to find several best suited to their region, season, and climate horizon of interest. Here we present methods to address both issues, model dependence and model subselection, to help users previously anchored in CMIP5 to navigate CMIP6. In Part I, we refine a definition of model dependence based on climate output, initially employed in Climate model Weighting by Independence and Performance (ClimWIP), to designate discrete model families within CMIP5/6. We show that the increased presence of model families in CMIP6 bolsters the upper mode of the ensemble's bimodal effective Equilibrium Climate Sensitivity (ECS) distribution. Accounting for the mismatch in representation between model families and individual model runs shifts the CMIP6 ECS median and 75th percentile down by 0.43 °C, achieving better alignment with CMIP5's ECS distribution. In Part II, we present a new, cost-function minimization-based approach to model subselection, Climate model Selection by Independence, Performance, and Spread (ClimSIPS), that selects sets of CMIP models based on the relative importance a user ascribes to model independence (as defined in Part I), model performance, and ensemble spread in projected climate outcome. We demonstrate ClimSIPS by selecting sets of three to five models from CMIP5/6 for European applications, evaluating the performance from the agreement with the observed mean climate, and the spread in outcome from the projected midcentury change in surface air temperature and precipitation. To accommodate different use cases, we explore two ways to represent models with multiple members in ClimSIPS, first, by ensemble mean and second, by an individual ensemble member that maximizes midcentury change diversity within CMIP overall. Because different combinations of models are selected by the cost function for different balances of independence, performance, and spread priority, we present all selected subsets in ternary contour "subselection triangles" and guide users with recommendations based on further qualitative independence, performance, and spread standards. In CMIP6, we find that recommended subsets are populated primarily by members of several model families defined in Part I due to an inverse relationship between performance and independence. In CMIP5, recommended subsets feature model combinations used in the European branch of the Coordinated Regional Downscaling Experiment (EURO-CORDEX), suggesting the independence, performance, and spread metrics used in ClimSIPS are appropriate for European applications in CMIP6 and beyond.

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Section: As Part Of the Earth Systemmentioning

confidence: 99%

Section: As Part Of the Earth Systemmentioning

confidence: 99%

Section: Model Families and Their Influence On Cmip Uncertaintymentioning

confidence: 99%

See 1 more Smart Citation

Climate model Selection by Independence, Performance, and Spread (ClimSIPS) for regional applications

Merrifield

Brunner

Lorenz

et al. 2023

Preprint

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“…Meteorological variables from six global climate models (GCMs)-CanESM5, GFDL-ESM4, IPSL-CM6A, MPI-ESM1-2, MRI-ESM2, and UKESM1 (Table 4)-along with the CO 2 concentration levels projected under three SSPs-SSP 1-2.6, 3-7.0, and 5-8.5-were used in this study for a total of 18 climate scenarios per location. Qian et al (2021) found that a well-selected subset of at least five GCMs could be representative of the larger ensemble of GCMs. These six GCMs are included in the up-to-date climate change 4), whereas three models (GFDL-ESM4, MPI-ESM1-2, and MRI-ESM2) lie close to the mode of the assessed distribution.…”

Section: Climate Change Simulationsmentioning

confidence: 99%

Spring barley yield and potential northward expansion under climate change in Canada

Jégo,

Crépeau,

Jing

et al. 2023

Agronomy Journal

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Spring barley (Hordeum vulgare L.), being a cold‐tolerant crop may not benefit as much from a warmer climate and a lengthening of the growing season due to climate change though its suitable production area could expand further north. The objectives of this study were to assess the impact of climate change on barley yields across Canada for both current production regions and potential northern crop expansion regions in the future. Three crop models (DNDC, DSSAT and STICS) and 18 climate scenarios (1981‐2100) were used to simulate the effect of climate change on potential (non‐water and non‐nitrogen limited) and rainfed (non‐nitrogen limited) spring barley yields for 32 locations across Canada. For the currently planted spring barley regions characterized by a humid summer in Eastern Canada (growing season precipitation > 500 mm), potential and rainfed yields were projected to slightly increase in future (<+0.2 t ha−1). In western regions where precipitation amount is lower (growing season precipitation <500 mm), changes in the potential yield varied slightly (‐0.1 to +0.2 t ha−1) while the rainfed yield was projected to increase (0.2 to 1.0 t ha−1) mainly due to a reduction in water stress under elevated CO2. Finally, in northern regions where future expansion may occur, projected yield increases were generally large (up to 2.8 t ha−1 for potential yield), but the risk of crop failure usually remained high. Our findings suggest that future climate change will present both opportunities and regionalized risks for spring barley producers with the potential to further expand barley production northward.This article is protected by copyright. All rights reserved

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“…Given the importance of wheat in human nutrition and global trade, many studies (e.g., [15][16][17]) are carried out across the world to assess the impact of CC on wheat yield. These studies used crop models and the Representative Concentrations Pathway scenarios (RCPs) [18].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Impact of Future Climate Change Impacts on Rainfed Durum Wheat Production in Algeria

Kourat

Smadhi

Madani

2022

Climate

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The predicted climate change threatens food security in the coming years in Algeria. So, this study aims to assess the impact of future climate change on a key crop in Algeria which is rainfed durum wheat. We investigate the impact of climate change on rainfed durum wheat cultivar called Mexicali using AquaCrop crop model and the EURO-CORDEX climate projections downscaled with the ICHEC_KNMI model under RCP 4.5 and RCP 8.5. A delta method was applied to correct the incertitudes present in the raw climate projections of two experimental sites located in Sétif and Bordj Bou Arreridj (BBA)’s Eastern High plains of Algeria (EHPs). AquaCrop was validated with a good precision (RMSE = 0.41 tha−1) to simulate Mexicali cultivar yields. In 2035–2064, it is expected at both sites: an average wheat grain yield enhances of +49% and +105% under RCP 4.5 and RCP 8.5, respectively, compared to the average yield of the baseline period (1981–2010), estimated at 29 qha−1. In both sites, in 2035–2064, under RCP 4.5 and RCP 8.5, the CO2 concentrations elevation has a fertilizing effect on rainfed wheat yield. This effect compensates for the negative impacts induced by the temperatures increase and decline in precipitation and net solar radiation. An increase in wheat water productivity is predicted under both RCPs scenarios. That is due to the water loss drop induced by the shortening of the wheat-growing cycle length by the effect of temperatures increase. In 2035–2064, early sowing in mid-September and October will lead to wheat yields improvement, as it will allow the wheat plant to benefit from the precipitations increase through the fall season. Thus, this early sowing will ensure a well vegetative development and will allow the wheat’s flowering and grain filling before the spring warming period.

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Effectiveness of using representative subsets of global climate models in future crop yield projections

Cited by 8 publications

References 29 publications

Climate model Selection by Independence, Performance, and Spread (ClimSIPS) for regional applications

Climate model Selection by Independence, Performance, and Spread (ClimSIPS) for regional applications

Spring barley yield and potential northward expansion under climate change in Canada

Modeling the Impact of Future Climate Change Impacts on Rainfed Durum Wheat Production in Algeria

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