Ensemble Of Global Climate Models Research Articles

Future malaria environmental suitability in Africa is sensitive to hydrology

Changes in climate shift the geographic locations that are suitable for malaria transmission because of the thermal constraints on vector Anopheles mosquitos and Plasmodium spp. malaria parasites and the lack of availability of surface water for vector breeding. Previous Africa-wide assessments have tended to solely represent surface water using precipitation, ignoring many important hydrological processes. Here, we applied a validated and weighted ensemble of global hydrological and climate models to estimate present and future areas of hydroclimatic suitability for malaria transmission. With explicit surface water representation, we predict a net decrease in areas suitable for malaria transmission from 2025 onward, greater sensitivity to future greenhouse gas emissions, and different, more complex, malaria transmission patterns. Areas of malaria transmission that are projected to change are smaller than those estimated by precipitation-based estimates but are associated with greater changes in transmission season lengths.

A Data-Driven Probabilistic Network Approach to Assess Model Similarity in CMIP Ensembles

Abstract The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple global climate models (GCMs) from different modeling centers with some shared building blocks and interdependencies. Applications typically follow the “model democracy” approach which might have significant implications in the resulting products (e.g., large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori approach seeks similarity in the GCMs’ output variables and is thus data-driven. In this study, we apply probabilistic network models (PNMs), a well-established machine learning technique, as a new a posteriori method to measure intermodel similarities. The proposed methodology is applied to surface temperature fields of the historical experiments from the CMIP5 multimodel ensemble and different reanalysis gridded datasets. PNMs are able to learn the complex spatial dependency structures present in climate data, including teleconnections operating on multiple spatial scales, characteristic of the underlying GCM. A distance metric building on the resulting PNMs is applied to characterize GCM model dependencies. The results of this approach are in line with those obtained with more traditional methods but have further explanatory potential building on probabilistic model querying. Significance Statement The present study proposes the use of probabilistic network models (PNMs) to quantify model similarity within ensembles of global climate models (GCMs). This is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. When applied to climate data (gridded global surface temperature in this study), PNMs encode the relevant spatial dependencies (local and remote connections). Similarities among the PNMs resulting from different GCMs can be quantified and are shown to capture similar GCM formulations reported in previous studies. Differently to other machine learning methods previously applied to this problem, PNMs are fully explainable (allowing probabilistic querying) and are applicable to high-dimensional gridded raw data.

State-of-the-art climate models reduce dominant dynamical uncertainty in projections of extreme precipitation

Extreme precipitation can lead to severe environmental and economic impacts. Thus, future changes in extreme precipitation and their uncertainties are of major interest. Changes in extreme precipitation can be decomposed into thermodynamic (temperature-related) and dynamic (vertical velocity related) contributions with a scaling approach for extreme precipitation. Applying this approach to the global climate model ensembles CMIP5 and CMIP6, we decompose projection uncertainties of extremes in daily precipitation into uncertainties of thermodynamic and dynamic changes. We analyze regional patterns of the total uncertainties in extreme precipitation projections, as well as the thermodynamic and dynamic contributions to these uncertainties. Total uncertainties relative to the projected multi model mean are dominated by the dynamical contributions, and are large over the tropics and subtropics, but smaller over the high and mid-latitudes. Uncertainties in the thermodynamic contribution are generally small. From CMIP5 to CMIP6, uncertainties in thermodynamic and dynamic changes are slightly reduced in the high and mid-latitudes, while there is a substantial reduction of the uncertainties of the dynamic changes in the tropics and subtropics.

Regional ensemble of CMIP6 global climate models for Sakha (Yakutia) Republic, Northern Eurasia

Future climate projections based on multi-model ensemble approach are seen as more reliable, but not all models are equally performant at reproducing climate features at a regional scale. An optimal regional GCM ensemble was developed for Sakha (Yakutia) Republic based on error statistics and spatial correlation metrics. Historical Coupled Model Intercomparison Project, version 6 (CMIP6) simulations from 48 global climate models (GCMs) were used to evaluate model quality compared to mean annual air temperature (MAAT) reanalysis data for 1961–1990, 1971–2000 and 1981–2010 reference periods, and the MAAT change between 1961-1990 and 1981–2010, ΔT81-61. The best-performing reanalysis, GHCN-CAMS, was validated using observational data. This five-member ensemble includes CESM2-WACCM, CMCC-ESM2, CNRM-CM6-1-HR, INM-CM5-0, MPI-ESM1-2-HR models, weighted by Pearson's coefficient of spatial correlation between observed and modeled ΔT81-61 fields. Model weighting based on spatial correlation metrics improved the performance of the developed multi-model regional ensemble, which can be used in projecting future climate under different climate change scenarios.

Band-by-band spectral radiative kernels based on the ERA5 reanalysis

Radiative kernel is a widely adopted method for diagnosing radiation variability and climate feedback. However, most of the existing radiative kernels are broadband flux kernels and lack the spectral information. Motivated by the growing interest in the spectral changes of the Earth radiation budget, we generate a new set of band-by-band radiative kernels based on the fifth generation European Center for Medium-Range Weather Forecasts (ERA5) reanalysis, which can be used for analyzing the spectrally decomposed changes in the top of atmosphere, surface and atmospheric radiation. The radiative sensitivity quantified by the ERA5 band-by-band kernel is compared to another spectral kernel and validated in a spectral radiation closure test. The use and benefits of the new ERA5 kernels are then demonstrated in an analysis of spectral feedbacks of an ensemble of global climate models (GCMs).

Modeling the impact of future rainfall changes on the effectiveness of urban stormwater control measures

The convergence of urban expansion, deteriorating infrastructure, and a changing climate will escalate the risks of stormwater pollution and urban flooding in the coming decades. Using outputs from an ensemble of global climate models to drive a high spatial resolution stormwater model, we analyzed climate change impacts on urban stormwater runoff and control measures for 23 cities across the United States. Runoff model outputs for two future emissions scenarios ending in 2055 were compared against a historical scenario to assess changes. All cities showed increases in average annual stormwater runoff, with changes up to 30% over the next 30 years due to a greater frequency of high intensity storm events. Runoff model outputs showed substantial variation across cities with untreated stormwater runoff increasing by as much as 48%. Patterns of future runoff impacts within cities will affect the performance of distributed treatment strategies such as Green Stormwater Infrastructure (GSI) to meet municipal water quality improvement and runoff reduction goals. Results indicate that adoption of adaptable design standards and decision support tools that readily accommodate projected precipitation changes are critical for supporting more resilient designs of stormwater control measures.

Effect of climate change on soil erosion rate in a tropical Indian catchment

Abstract This study analyzes the variations in soil erosion rate in a tropical catchment in India using the empirical Revised Universal Soil Loss Equation (RUSLE) model integrated with climate change scenarios from an ensemble of global climate models (GCMs) included in the NEX Global Daily Downscaled Projections Coupled Model Intercomparison Project Phase 6 dataset for Shared Socioeconomic Pathways (SSP) 126 and SSP 585. A set of seven GCMs are initially selected. Based on their ability to simulate the rainfall for the current scenario, the PROMETHEE-II method is used to rank the GCMs and the top four best-performing models are used for further analysis. Soil erosion rates projected for the future climate scenarios are compared with the current scenario. In the near future and in the middle of the century, soil erosion rates under the SSP 126 scenario are projected to be higher than that under the SSP 585 scenario. The ensemble average soil erosion rate is projected to increase by 15.41%–25.94% toward the end of the century for different emission scenarios, and the areas susceptible to high and very high soil erosion rates are projected to increase to 40.3%.

Wind energy potential of weather systems affecting South Africa’s Eastern Cape Province

AbstractAs a percentage of the total global energy supply, wind energy facilities could provide 10% of the total global energy supply by 2050 as reported in IEA World Energy Outlook (2022). Considering this, a just transition to renewable and sustainable energy in South Africa is a genuine possibility if steps are taken immediately to achieve this. The Eastern Cape Province exhibits a strong wind resource which can be exploited towards expediting such a just energy transition. No research and related modelling have, to date, been undertaken in quantifying and relating the detailed P50 energy yield analyses of representative wind energy facilities in temporal and spatial dimensions to the occurrence of specific synoptic types in South Africa. To quantify this energy meteorology climatology for a suitably sized geospatial area in the Eastern Cape Province of South Africa (spatial focus area, latitude −30 to −35, longitude 20 to 30), the approach of using self-organising maps is proposed. These maps are used to identify the most common synoptic circulation types occurring in the Eastern Cape and can subsequently be mapped onto an equivalent time resolution wind energy production timeseries calculated based on probable wind energy facility sites. This paper describes comprehensive methodologies used to model the wind energy facilities, calculate with high confidence the P50 energy production, and then identify the predominant synoptic weather types responsible for the wind energy production in this spatial focus area. After quantifying the energy production, running a self-organising map software generates a purposely selected 35 node map that characterises archetypal synoptic patterns over the 10-year period. The synoptic types can be ranked by the highest energy production. It is shown that in this spatial area, monthly wind energy production is higher during the winter months. When the well-established high-pressure cells move northward, synoptic types associated with higher energy production are frequent and include tropical and temperate disturbances across South Africa, patterns resembling a ridging anticyclone off the west coast of South Africa and low-pressure cells occurring to the north and south. Low energy producing patterns show characteristics of the high-pressure cells moving southwards producing fine weather and mildly disturbed conditions. The purpose of this methodology is that it provides the foundation required to derive long-term frequency changes of these synoptic weather systems using global climate model ensembles and thus changes in wind energy production.

Combined impacts of climate and land-use change on future water resources in Africa

Abstract. Africa depends on its water resources for hydroelectricity, inland fisheries and water supply for domestic, industrial and agricultural operations. Anthropogenic climate change (CC) has changed the state of these water resources. Land use and land cover have also undergone significant changes due to the need to provide resources to a growing population. Yet, the impact of the land-use and land cover change (LULCC) in addition to CC on the water resources of Africa is underexplored. Here we investigate how precipitation, evapotranspiration (ET) and river flow respond to both CC and LULCC scenarios across the entire African continent. We set up a Soil and Water Assessment Tool (SWAT+) model for Africa and calibrated it using the hydrological mass balance calibration (HMBC) methodology detailed in Chawanda et al. (2020a). The model was subsequently driven by an ensemble of bias-adjusted global climate models to simulate the hydrological cycle under a range of CC and LULCC scenarios. The results indicate that the Zambezi and the Congo River basins are likely to experience reduced river flows under CC with an up to 7 % decrease, while the Limpopo River will likely have higher river flows. The Niger River basin is likely to experience the largest decrease in river flows in all of Africa due to CC. The Congo River basin has the largest difference in river flows between scenarios with (over 18 % increase) and without LULCC (over 20 % decrease). The projected changes have implications for the agriculture and energy sectors and hence the livelihood of people on the continent. Our results highlight the need to adopt policies to halt global greenhouse gas emissions and to combat the current trend of deforestation to avoid the high combined impact of CC and LULCC on water resources in Africa.

Assessing Water Resource Sustainability in the Kabul River Basin: A Standardized Runoff Index and Reliability, Resilience, and Vulnerability Framework Approach

The sustainability of water resources is fundamental for basin management, especially in regions where changing hydrological conditions due to climate extremes are prevalent. This study presents a comprehensive assessment of the Kabul River Basin (KRB) sustainability using the Standardized Runoff Index (SRI) as a runoff indicator. By integrating the concepts of reliability, resilience, and vulnerability (RRV), this research aims to provide a granular understanding of water sustainability within the basin. Utilizing future climate projections derived from the mean ensemble of Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5), the Soil and Water Assessment Tool (SWAT) model simulated surface runoff for the KRB. Employing a grid-based approach, this study uncovers the internal variabilities within the basin, recognizing that individual grid points may present distinct water availability characteristics. Historical analysis indicated a decline in the KRB’s sustainability, attributed to increased vulnerability and reduced reliability and resilience. Future projections emphasize the basin’s susceptibility to runoff variations, particularly in downstream areas characterized by dense populations and intense agricultural activities. These findings accentuate the need for strategic water resource management within the KRB to address localized vulnerabilities and ensure sustainable water availability amidst climatic uncertainties.

Global and Regional Climate Feedbacks in Response to Uniform Warming and Cooling

AbstractWe compare the radiative feedbacks in an ensemble of global climate models under uniform 4 K warming and cooling of sea surface temperatures. The global‐mean net feedback is less stabilizing under warming in all nine models. This results primarily from less stabilizing water vapor and extra‐tropical shortwave cloud feedbacks, partially offset by more stabilizing tropical cloud feedbacks. The zonal‐mean feedbacks are also robust across the ensemble. In the extra‐tropics, the less stabilizing shortwave cloud feedback under warming is associated with further poleward migration of the mean Southern Hemisphere jet in some models. However, additional experiments with an aquaplanet version of the HadGEM3 model suggest that the asymmetry of the jet shift is not driving the asymmetry in the cloud feedbacks at these latitudes. In the tropics, the stronger water vapor feedback under warming is offset by weaker shortwave cloud feedbacks. The result is that the ensemble spread in the differences between the global feedbacks under warming and cooling is mainly driven by their differences in the tropics. The spatial distribution of the feedbacks largely reflects the zonal‐mean behavior, although there is considerable model spread in the regional cloud feedbacks, particularly in the tropical shortwave cloud feedback. Comparison with CO2‐ and solar‐forced coupled experiments suggests that the global‐mean longwave cloud feedback is nearly invariant to warming and cooling, regardless of the nature of the forcing. The shortwave cloud feedback is generally more positive under warming in the coupled models, consistent with the uniform SST perturbation experiments.

Historical evaluation and future projections of compound heatwave and drought extremes over the conterminous United States in CMIP6 * *Notice: This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). Accordingly, the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide

Independently, both droughts and heatwaves can induce severe impacts on human and natural systems. However, when these two climate extremes occur concurrently in a given region, their compound impacts are often more pronounced. With the improvement in both the spatiotemporal resolution and representation of complex climate processes in the global climate models (GCMs), they are increasingly used to study future changes in these extremes and associated regional impacts. However, GCM selection for such impact assessments is generally based on historical performance and/or future mean changes, without considering individual or compound extremes. In contrast, this study evaluates historical performance and projected changes in heatwaves, droughts, and compound heatwave-droughts using an ensemble of GCMs from the latest Phase 6 of Coupled Models Intercomparison Project at a regional scale across the conterminous United States. Additionally, we explore the inter-model differences in the projected changes that are associated with various characteristics of extremes and the choice of drought indices. Our analysis reveals considerable variation among the GCMs, as well as substantial differences in the projected changes based on the choice of drought indices and region of interest. For example, the projected increases in both the frequency and intensity of drought and associated compound extreme days, based on the standardized precipitation evapotranspiration index far exceed those derived from the standard precipitation index. Further, the largest changes in the frequency of compound extremes are projected over the Southwest, South Central, and parts of the Southeast while the smallest changes are projected over the Northeast. Overall, this study provides important insights for the interpretation and selection of GCMs for future assessment studies that are crucial for the development of regional adaptation strategies in the face of climate change.

Monte Carlo drift correction – quantifying the drift uncertainty of global climate models

Abstract. Global climate models are susceptible to drift, causing spurious trends in output variables. Drift is often corrected using data from a control simulation. However, internal climate variability within the control simulation introduces uncertainty to the drift correction process. To quantify this drift uncertainty, we develop a probabilistic technique: Monte Carlo drift correction (MCDC). MCDC samples the standard error associated with drift in the control time series. We apply MCDC to an ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that drift correction partially addresses a problem related to drift: energy leakage. Nevertheless, the energy balance of several models remains suspect. We quantify the drift uncertainty of global quantities associated with the Earth's energy balance and thermal expansion of the ocean. When correcting drift in a cumulatively integrated energy flux, we find that it is preferable to integrate the flux before correcting the drift: an alternative method would be to correct the bias before integrating the flux, but this alternative method amplifies the drift uncertainty. Assuming that drift is linear likely leads to an underestimation of drift uncertainty. Time series with weak trends may be especially susceptible to drift uncertainty: for historical thermosteric sea level rise since the 1850s, the drift uncertainty can range from 3 to 24 mm, which is of comparable magnitude to the impact of omitting volcanic forcing in control simulations. Derived coefficients – such as the ocean's expansion efficiency of heat – can also be susceptible to drift uncertainty. When evaluating and analysing global climate model data that are susceptible to drift, researchers should consider drift uncertainty.

Improving the quantification of climate change hazards by hydrological models: a simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration

Abstract. Almost no hydrological model takes into account that changes in evapotranspiration are affected by how vegetation responds to changing CO2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and, thus, water resources. As the simulation of vegetation responses is both complex and very uncertain, we recommend a simple approach to considering (in climate change impact studies with hydrological models) the uncertainty that the vegetation response causes with respect to the estimation of future potential evapotranspiration (PET). To quantify this uncertainty in a simple manner, we propose running the hydrological model in two variants: with its standard PET approach and with a modified approach to compute PET. In the case of PET equations containing stomatal conductance, the modified approach can be implemented by adjusting the conductance. We introduce a modified approach for hydrological models that computes PET as a function of net radiation and temperature only, i.e., with the Priestley–Taylor (PT) equation. The new PT-MA approach is based on the work of Milly and Dunne (2016) (MD), who compared the change in non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCMs), which simulate vegetation response as well as interactions between the atmosphere and the land surface, with various methods to compute PET change. Based on this comparison, MD proposed estimating the impact of climate change on PET as a function of only the change in net energy input at the land surface. PT-MA retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented PT-MA in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs. Increases in GCM-derived NWSAET between the end of the 20th and the end of the 21st century for Representative Concentration Pathway 8.5 (RCP8.5) are simulated well by WaterGAP if PT-MA is applied but are severely overestimated with the standard PT method. Application of PT-MA in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (expressed as the variable RWR) compared with the standard PT method, except in a small number of grid cells where increased inflow from upstream areas due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated fields. On about 20 % of the global land area, PT-MA leads to an increase in RWR that is more than 20 % higher than in the case of standard PT, while on more than 10 % of the global land area, the projected RWR decrease is reduced by more than 20 %. While the modified approach to compute PET is likely to avoid the overestimation of future drying in many if not most regions, the vegetation response in other regions may be such that the application of the standard PET leads to more likely changes in PET. As these regions cannot be identified with certainty, the proposed ensemble approach with two hydrological model variants serves to represent the uncertainty in hydrological changes due to the vegetation response to climate change that is not represented in the model.

Projected Changes in Precipitation Based on the CMIP6 Optimal Multi-Model Ensemble in the Pearl River Basin, China

Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p < 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin.

Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions

Climate change can exacerbate the occurrence of extreme precipitation events, thereby affecting both the frequency and intensity of rainfall-induced landslides. It is important to study the threat of rainfall-induced landslides under future climate conditions for the formulation of disaster prevention and mitigation policies. Due to the complexity of the climate system, there is great uncertainty in the climate variables simulated by a global climate model (GCM), which will be further propagated in landslide prediction. In this study, we investigate the spatial and temporal trends of future landslide hazards in China under climate change, using data from a multi-model ensemble of GCMs based on two scenarios, RCP4.5 and RCP8.5. The uncertainty characteristics are then estimated based on signal-to-noise ratios (SNRs) and the ratio of agreement in sign (RAS). The results show that the uncertainty of landslide prediction is mainly dominated by the GCM ensemble and the RCP scenario settings. Spatially, the uncertainty of landslide prediction is high in the western areas of China and low in the eastern areas of China. Temporally, the uncertainty of landslide prediction is evolving, with characteristics of high uncertainty in the near future and characteristics of low uncertainty in the distant future. The annual average SNRs in the 21st century are 0.44 and 0.50 in RCP4.5 and RCP8.5, respectively, and the RAS of landslide prediction in Southeastern China is only 50–60%. This indicates that more than half of the patterns show trends that are opposite to those of the ensemble, suggesting that their landslide change trends are not universally recognized in the pattern ensemble. Considering the uncertainty of climate change in landslide prediction can enable studies to provide a more comprehensive picture of the possible range of future landslide changes, effectively improving the reliability of landslide hazard prediction and disaster prevention.

Future heat extremes and impacts in a convection-permitting climate ensemble over Germany

Abstract. Heat extremes and associated impacts are considered the most pressing issue for German regional governments with respect to climate adaptation. We explore the potential of a unique high-resolution, convection-permitting (2.8 m), multi-GCM (global climate model) ensemble with COSMO-CLM (Consortium for Small-scale Modeling Climate Limited-area Modelling) regional simulations (1971–2100) over Germany regarding heat extremes and related impacts. We find a systematically reduced cold bias especially in summer in the convection-permitting simulations compared to the driving simulations with a grid size of 7 km and parametrized convection. The projected increase in temperature and its variance favors the development of longer and hotter heat waves, especially in late summer and early autumn. In a 2 ∘C (3 ∘C) warmer world, a 26 % (100 %) increase in the heat wave magnitude index is anticipated. Human heat stress (universal thermal climate index (UTCI) > 32 ∘C) and region-specific parameters tailored to climate adaptation revealed a dependency on the major landscapes, resulting in significantly higher heat exposure in flat regions such as the Rhine Valley, accompanied by the strongest absolute increase. A nonlinear, exponential increase is anticipated for parameters characterizing strong heat stress (UTCI > 32 ∘C, tropical nights, very hot days). Providing region-specific and tailored climate information, we demonstrate the potential of convection-permitting simulations to facilitate improved impact studies and narrow the gap between climate modeling and stakeholder requirements for climate adaptation.

Revisiting the Potential to Narrow Model Uncertainty in the Projections of Arctic Runoff

AbstractDespite multiple advances in the understanding of the water cycle intensification in a warmer climate, climate models still diverge in their hydrological projections. Here we constrain annual runoff projections over individual and aggregated Arctic river basins. For this purpose, we use two ensembles of global climate models and two statistical methods: a regression scheme assuming similar runoff sensitivities at interannual versus climate change timescales, and a Bayesian method where models are used to derive a posterior runoff response conditioned on historical observations. While both techniques are shown to narrow model uncertainties, more or less substantially depending on rivers, the Bayesian method is less sensitive to the choice of the model ensemble and is more skillful when tested with synthetic observations. It has also been applied over the whole Arctic watershed, showing so far a limited narrowing of the inter‐model spread, but its skill will further improve with increasing climate change.

An Approach for Selecting Observationally-Constrained Global Climate Model Ensembles for Regional Climate Impacts and Adaptation Studies in Canada

Given the growing number of global climate models (GCMs) with simulations available for impacts and adaptation studies, methods have been introduced to select models that are ‘fit-for-purpose’. This study applies a GCM selection process to historical and future climate projections from 38 and 43 GCMs contributing to the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Models are selected based on historical performance, with a further selection step targeted at reducing interdependencies between closely related model variants and ensemble members. Ten performance measures are calculated based on climatological statistics (mean, standard deviation, and seasonal cycle) of three climate variables (precipitation, sea level pressure, and surface air temperature (SAT)), as well as SAT warming trend for the 1985–2014 period. Performance is assessed over Canada and six Canadian sub-regions, at both annual and seasonal timescales. As initial-condition members and minor variants of GCMs are not independent, a representative democracy approach – using ensemble averages of initial-condition members and including only the best performance model among minor variants – is employed to reduce redundancy in selected subsets. There is a strong correlation between recent warming trends and future warming projections across Canada; therefore, observed SAT warming trends are recognized as important observational-constraints to aid in model selection. By removing “hot models” that fail to reproduce the historical SAT warming trend, a representative subset of observationally-constrained GCMs projects lower annual SAT than model democracy (using all model runs assuming independence and equal plausibility) over Canada and six Canadian sub-regions for 2071–2100.

Modeling climate change impact on dryland wheat production for increased crop yield in the Free State, South Africa, using GCM projections and the DSSAT model

Introduction: The impact of climate change on food production in South Africa is likely to increase due to low rainfall and frequent droughts, resulting in food insecurity in the future. The use of well-calibrated and validated crop models with climate change data is important for assessing climate change impacts and developing adaptation strategies. In this study, the decision support system for agrotechnology transfer (DSSAT) crop model was used to predict yield using observed and projected climate data.Materials and Methods: Climate, soil, and crop management data were collected from wheat-growing study sites in Bethlehem, South Africa. The DSSAT wheat model (CROPSIM-CERES) used was already calibrated, and validated by Serage et al. (Evaluating Climate Change Adaptation Strategies for Disaster Risk Management: Case Study for Bethlehem Wheat Farmers, South Africa, 2017) using three wheat cultivar coefficients obtained from the cultivar adaptation experiment by the ARC-Small Grain Institute. The model was run with historical climate data for the eastern Free State (Bethlehem) from 1999 to 2018 as the baseline period. To determine the effects of climate change, the crop model simulation for wheat was run with future projections from four Global Climate Models (GCM): BCC-CSM1_1, GFDL-ESM2G, ENSEMBLE, and MIROC from 2020 to 2077.Results: The average wheat yield for the historic climate data was 1145.2 kg/ha and was slightly lower than the highest average yield of 1215.9 kg/ha from GCM ENSEMBLE during Representative concentration pathways (RCP) 2.6, while the lowest yield of 29.8 kg/ha was produced during RCP 8.5 (GCM GFDL-ESM2G). Model GFDL-ESM2G produced low yields (29.8–47.74 kg/ha) during RCP 8.5 and RCP 6.0, respectively. The yield range for GCM BCC-CSM1_1 was 770.2 kg/ha during RCP 2.6 to 921.68 kg/ha during RCP 4.5 and 547.84 kg/ha during RCP 8.5 to 700.22 kg/ha during RCP 2.6 for GCM MIROC.Conclusion: This study showed a declining trend in yield for future climate projections from RCP2.6 to RCP8.5, indicating that the possible impacts of higher temperatures and reduced rainfall in the projected future climate will slightly decrease wheat production in the eastern Free State. Adaptation measures to mitigate the potential impact of climate change could include possible changes in planting dates and cultivars. Using a crop model to simulate the response of crops to variations in weather conditions can be useful to generate advisories for farmers to prevent low yield.