Ensemble Of General Circulation Models Research Articles

Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia

The severity and frequency of extremes are changing; thus, it is becoming necessary to evaluate the impacts of land cover changes and urbanisation along with climate change. A framework of the Generalised Extreme Value (GEV) method, Google Earth Engine (GEE), and land cover patterns’ classification including Random Forest (RF) and Support Vector Machine (SVM) can be useful for streamflow impact analysis. For this study, we developed a unique framework consisting of a hydrological model in line with the Process-informed Nonstationary Extreme Value Analysis (ProNEVA) GEV model and an ensemble of General Circulation Models (GCMs), mapping land cover patterns using classification methods within the GEE platform. We applied these methods in Southeast Queensland (SEQ) to analyse the maximum instantaneous floods in non-stationary catchment conditions, considering the physical system in terms of cause and effect. Independent variables (DEM, population, slope, roads, and distance from roads) and an integrated RF, SVM methodology were utilised as spatial maps to predict their influences on land cover changes for the near and far future. The results indicated that physical factors significantly influence the layout of landscapes. First, the values of projected evapotranspiration and rainfall were extracted from the multi-model ensemble to investigate the eight GCMs under two climate change scenarios (RCP4.5 and RCP8.5). The AWBM hydrological model was calibrated with daily streamflow and applied to generate historical runoff for 1990–2010. Runoff was projected under two scenarios for eight GCMs and by incorporating the percentage of each land cover into the hydrological model for two horizons (2020–2065 and 2066–2085). Following that, the ProNEVA model was used to calculate the frequency and magnitude of runoff extremes across the parameter space. The maximum peak flood differences under the RCP4.5 and RCP8.5 scenarios were 16.90% and 15.18%, respectively. The outcomes of this study suggested that neglecting the non-stationary assumption in flood frequency can lead to underestimating the amounts that can lead to more risks for the related hydraulic structures. This framework is adaptable to various geographical regions to estimate extreme conditions, offering valuable insights for infrastructure design, planning, risk assessment, and the sustainable management of future water resources in the context of long-term water management plans.

Inter‐Comparison of Precipitation Simulation and Future Projections Over China From an Ensemble of Multi‐GCM Driven RCM Simulations

AbstractAn analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing.

Numerical simulations of recent and future evolution of monte perdido glacier

Glaciers are globally retreating due to climate change, and the Pyrenees Mountain range is no exception. This study uses the Open Global Glacier Model (OGGM) to explore the dynamics of the Monte Perdido glacier, one of the largest remaining glaciers in the Pyrenees. We explored three calibration approaches to assess their performances when reproducing observed volume decreases. The first approach involved mass balance calibration using terrestrial laser scanning data from 2011 to 2022 and climate data from a nearby weather station. The second approach used terrestrial laser scanning calibration with default climate data provided by OGGM (GSWP3-W5E5). The third approach used default geodetic mass balance calibration and default climate data. By comparing these calibration strategies and analysing historical data (terrestrial laser scanning and ground penetrating radar), we obtain insights of the applicability of OGGM to this small, mild conditions, Pyrenean glacier. The first calibration approach is identified as the most effective, emphasising the importance of selecting appropriate climate data and calibration methods. Additionally, we conducted future volume projections using an ensemble of General Circulation Models (GCMs) under the RCP2.6 and RCP8.5 scenarios. The results indicate a potential decrease in total ice volume ranging from 91.60% to 95.16% by 2100, depending on the scenario. Overall, this study contributes to the understanding of the Monte Perdido glacier’s behaviour and its response to climate change through the calibration of the OGGM, while also providing the first estimate of its future melting under different emission scenarios.

A Global Map for Selecting Stationary and Nonstationary Methods to Estimate Extreme Floods

Comprehending the changing patterns of flood magnitudes globally, particularly in the context of nonstationary conditions, is crucial for effective flood risk management. This study introduces a unique approach that employs simulated discharge data to unravel these intricate variations. Through a comprehensive analysis of a substantial ensemble of General Circulation Models (GCMs) runoff datasets, we examine the dynamics of nonstationary flood magnitudes on a global scale. A pivotal aspect of our investigation is the development of a reference map, which helps delineate suitable scenarios for applying stationary or nonstationary methods in estimating extreme floods. This map is then employed to compare estimations of 100-year flood magnitudes using both methodologies across specific geographical areas. Our findings distinctly highlight the disparities arising from the use of stationary versus nonstationary approaches for estimating extreme floods. These insights underscore the significance of considering nonstationary for accurate flood risk assessment and mitigation strategies. The practical utility of our reference map in aiding informed decision making for stakeholders and practitioners further underscores its importance. This study contributes to the scholarly understanding of the evolving nature of flood phenomena and provides valuable insights for crafting adaptive measures in response to changing climatic conditions.

Fingerprinting Low-Frequency Last Millennium Temperature Variability in Forced and Unforced Climate Models

Abstract Constraining unforced and forced climate variability impacts interpretations of past climate variations and predictions of future warming. However, comparing general circulation models (GCMs) and last millennium Holocene hydroclimate proxies reveals significant mismatches between simulated and reconstructed low-frequency variability at multidecadal and longer time scales. This mismatch suggests that existing simulations underestimate either external or internal drivers of climate variability. In addition, large differences arise across GCMs in both the magnitude and spatial pattern of low-frequency climate variability. Dynamical understanding of forced and unforced variability is expected to contribute to improved interpretations of paleoclimate variability. To that end, we develop a framework for fingerprinting spatiotemporal patterns of temperature variability in forced and unforced simulations. This framework relies on two frequency-dependent metrics: 1) degrees of freedom (≡N) and 2) spatial coherence. First, we use N and spatial coherence to characterize variability across a suite of both preindustrial control (unforced) and last-millennium (forced) GCM simulations. Overall, we find that, at low frequencies and when forcings are added, regional independence in the climate system decreases, reflected in fewer N and higher coherence between local and global mean surface temperature. We then present a simple three-box moist-static-energy-balance model for temperature variability, which is able to emulate key frequency-dependent behavior in the GCMs. This suggests that temperature variability in the GCM ensemble can be understood through Earth’s energy budget and downgradient energy transport, and allows us to identify sources of polar-amplified variability. Finally, we discuss insights the three-box model can provide into model-to-model GCM differences. Significance Statement Forced and unforced temperature variability are poorly constrained and understood, particularly that at time scales longer than a decade. Here, we identify key differences in the time scale–dependent behavior of forced and unforced temperature variability using a combination of numerical climate models and principles of downgradient energy transport. This work, and the spatiotemporal characterizations of forced and unforced temperature variability that we generate, will aid in interpretations of proxy-based paleoclimate reconstructions and improve mechanistic understanding of variability.

Conceptual Sim-Heuristic optimization algorithm to evaluate the climate impact on reservoir operations

This study covers the application of sim-heuristics to simulate and optimise the KLang Gate Dam (KGD) operating rule curve using the Coupled Model Intercomparison Project 5 (CMIP5) climate scenarios. This research aims to examine future climate change impacts on the KGD reservoir water resources. First, based on model institution location and data availability, a few General Circulation Models (GCMs) under the CMIP5 were chosen. Most earlier studies had solely examined the impact of climate change on future reservoir operations using a single GCM. The ensemble of GCMs for precipitation, temperature (Maximum, Minimum, and Mean), and solar radiation for the base period (1991–2005) and future climatic scenarios under the Representative Concentration Pathways, RCP 2.6, RCP 4.5, and RCP 8.5 were downscaled, trained, and tested using data-driven techniques namely; the Artificial Neural Network (ANN) and the Support Vector Regression (SVR). During the base period, the SVR (Poly function) achieved R performance values of 0.6201, 0.5743, 0.6926, and 0.6073 for the respective predictant variables. Upon addressing for rainfall-runoff, the Turc-radiation evaporation strategy was utilised at this study location since it was suitable for the tropical, humid, or sub-humid region. Few scenarios were developed to forecast water demand. Scenario 1 was based on the base period (1991–2005) of water demand, whereas Scenarios 2 and 3 were based on maximum and mean temperatures, respectively (2020–2099). The results were then evaluated in terms of storage failure, reliability, resilience, and vulnerability. Overall, Scenario 3 showed the greatest reliability in satisfying exact demand with 93.54 %, as well as the least shortage index and length of water deficit under RCP 4.5.

General circulation models for rainfall simulations: Performance assessment using complex networks

Reliable assessment of the impact of climate change on hydrology in a region requires proper selection of General Circulation Models (GCMs). The present study introduces the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall and selecting an ensemble of the best-performing GCMs. The performance of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating monthly rainfall over India is assessed. The observed (interpolated and gridded rainfall provided by the India Meteorological Department) rainfall data and GCM-simulated rainfall data over the period 1961–2014 at a spatial resolution of 1° x 1° (a total of 288 grids) are studied. The GCMs are evaluated for two cases: (1) Case 1 – whole year rainfall; and (2) Case 2 – summer monsoon rainfall (June–September). The clustering coefficient of the rainfall network is used as a network measure to evaluate the ability of the GCMs in simulating rainfall. For rainfall network construction, each grid is considered as a network. The phase space reconstruction concept is used to reconstruct the single-variable rainfall time series in a multi-dimensional phase space to represent the rainfall dynamics. The optimal dimension for reconstruction is determined using the false nearest neighbor (FNN) algorithm. For network construction, each reconstructed vector is considered as a node, and the connections between them, identified using a distance threshold for the reconstructed vectors in the phase space, serve as the links. For each of the 288 grids, the GCMs are ranked based on the difference in the clustering coefficient between the observed and GCM-simulated rainfall networks. The group decision-making (GDM) approach is employed to identify the ensemble of the best-performing GCMs for the entire study area considering all the grids. The results suggest different models perform well for the whole-year rainfall and summer monsoon rainfall. For the whole-year rainfall, the models CMCC-CM2-HR4, GFDL-ESM4, CMCC-ESM2, EC-Earth3-AerChem, and CMCC-CM2-SR5 rank as the top five. For the summer monsoon rainfall, FIO-ESM-2-0, E3SM-1-1, CESM2-FV2, CMCC-CM2-HR4, and CMCC-CM2-SR5 perform the best. Considering the rank of each model for both the cases, the best-performing models are identified to be CMCC-CM2-HR4, CMCC-CM2-SR5, CMCC-ESM2, FIOESM-2-0, and E3SM-1-1. The rainfall simulated from these models also show close resemblance to the observed rainfall, especially in terms of monthly mean rainfall. Therefore, even among the many GCMs that show good agreement with the monthly mean observed rainfall, the clustering coefficient-based analysis helps to narrow down the models for the ensemble of best-performing GCMs.

Simulating the potential effects of elevated CO2 concentration and temperature coupled with storm intensification on crop yield, surface runoff, and soil loss based on 25 GCMs ensemble: A site-specific case study in Oklahoma

Proper simulation of storm intensification is critical in projecting crop yield, surface runoff, and soil loss under climate change conditions. We developed a total of 100 climate scenarios coupled with storm intensification, which were based on 25 downscaled GCMs (General Circulation Model) projections under RCP4.5 and RCP8.5 (Representative Concentration Pathways) for two time periods of 2021–2050 and 2051–2080. The climate files were applied to a modified WEPP (Water Erosion Prediction Project) model to estimate crop yield, runoff, and soil loss under 29 combinations of cropping and tillage systems. The results showed that future extreme storm events in the study area would significantly increase during 2021–2080 (P < 0.01). However, average monthly precipitation in summer would decrease by 12.5% in June and 10.8% in July, along with an annual precipitation decline of 2.6%, leading to a decrease in crop yield in this rainfed agricultural region. The amount of runoff (soil loss) from extreme storms would account for 45.9 (64.3%) of the total annual runoff (soil loss) when averaged across two time periods and two RCPs. The average annual runoff depth (soil loss amount) from crop rotation or double cropping systems would be 27.6 (78.1%) less than the corresponding values in a continuous monoculture cropping system. No-till and crop rotations with alfalfa are the best agricultural management alternatives to mitigate soil erosion rates under future climate change. The analysis of variance (ANOVA) indicated that the uncertainty contributions of GCMs and cropping and tillage systems reached 45.5% and 30.4% in annual surface runoff prediction (P < 0.001), while cropping and tillage systems (71.7%, P < 0.001) was the major uncertainty source in annual soil loss simulations. This study improved the prediction of crop yield, runoff, and soil loss from various cropping and tillage systems under climate change conditions by integrating storm intensification from multiple GCM ensembles.

Modeling surface runoff and soil loss response to climate change under GCM ensembles and multiple cropping and tillage systems in Oklahoma

To develop effective conservation practices to respond to future climatic challenges, the effects of various cropping and tillage systems on surface runoff and soil loss need to be evaluated under extensive geographical conditions. This study used a total of 100 climate scenarios generated from 25 downscaled General Circulation Model (GCM) projections under two Representative Concentration Pathways (RCP4.5 and 8.5) during 2021–2050 and 2051–2080. Those 100 future scenarios were combined with 29 cropping and tillage systems to simulate surface runoff, soil erosion, and crop production response to climate change using the Water Erosion Prediction Project (WEPP) model. The results showed that average annual precipitation in central Oklahoma was projected to significantly decrease by 4–6% (p < 0.05) during the two time periods for both RCP scenarios. Mean annual temperatures were projected to significantly increase (p < 0.01) by 1.74 ℃ for RCP4.5 and 1.99 ℃ for RCP8.5 during 2021–2050, and 2.65 ℃ for RCP4.5 and 3.90 ℃ for RCP8.5 during 2051–2080. Annual runoff and soil loss averaged over the two RCPs and all crop types was projected to decrease by approximately 1% and 3%, respectively. Except for cotton, crop yields were predicted to decrease by 10.3%–18.3% during 2021–2080. Simulated annual runoff depth and soil loss separately followed the order of reduced tillage (RT) > delayed tillage (DT) > no-till (NT) > conventional tillage (CT) and RT > CT > DT > NT under future climate scenarios, respectively. If economically feasible, no-till and the crop-alfalfa rotation were the most effective soil conservation method on farmlands to combat projected future erosion due to changing precipitation and temperatures.

EFSO at Different Geographical Locations Verified with Observing System Experiments

AbstractAn ensemble-based forecast sensitivity to observations (EFSO) diagnosis has been implemented in an atmospheric general circulation model–ensemble Kalman filter data assimilation system to estimate the impacts of specific observations from the quasi-operational global observing system on weekly short-range forecasts. It was examined whether EFSO reasonably approximates the impacts of a subset of observations from specific geographical locations for 6-h forecasts, and how long the 6-h observation impacts can be retained during the 7-day forecast period. The reference for these forecasts was obtained from 12 data-denial experiments in each of which a subset of three radiosonde observations launched from a geographical location was excluded. The 12 locations were selected from three latitudinal bands comprising (i) four Arctic regions, (ii) four midlatitude regions in the Northern Hemisphere, and (iii) four tropical regions during the Northern Hemisphere winter of 2015/16. The estimated winter-averaged EFSO-derived observation impacts well corresponded to the 6-h observation impacts obtained by the data denials and EFSO could reasonably estimate the observation impacts by the data denials on short-range (from 6 h to 2 day) forecasts. Furthermore, during the medium-range (4–7 day) forecasts, it was found that the Arctic observations tend to seed the broadest impacts and their short-range observation impacts could be projected to beneficial impacts in Arctic and midlatitude North American areas. The midlatitude area was located just downstream of dynamical propagation from the Arctic toward the midlatitudes. Results obtained by repeated Arctic data-denial experiments were found to be generally common to those from the non-repeated experiments.

Heterogeneous Changes to Wetlands in the Canadian Prairies Under Future Climate

AbstractNumerous wetlands in the prairies of Canada provide important ecosystem services, yet are threatened by climate and land‐use changes. Understanding the impacts of climate change on prairie wetlands is critical to effective conservation planning. In this study, we construct a wetland model with surface water balance and ecoregions to project future distribution of wetlands. The climatic conditions downscaled from the Weather Research and Forecasting model were used to drive the Noah‐MP land surface model to obtain surface water balance. The climate change perturbation is derived from an ensemble of general circulation models using the pseudo global warming method, under the RCP8.5 emission scenario by the end of 21st century. The results show that climate change impacts on wetland extent are spatiotemporally heterogenous. Future wetter climate in the western Prairies will favor increased wetland abundance in both spring and summer. In the eastern Prairies, particularly in the mixed grassland and mid‐boreal upland, wetland areas will increase in spring but experience enhanced declines in summer due to strong evapotranspiration. When these effects of climate change are considered in light of historical drainage, they suggest a need for diverse conservation and restoration strategies. For the mixed grassland in the western Canadian Prairies, wetland restoration will be favorable, while the highly drained eastern Prairies will be challenged by the intensified hydrological cycle. The outcomes of this study will be useful to conservation agencies to ensure that current investments will continue to provide good conservation returns in the future.

Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Abstract. General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Apart from the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- and large-scale variables. This study compares four statistical downscaling methods of bias correction (BC), the change factor of mean (CFM), quantile perturbation (QP) and an event-based weather generator (WG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000 for the weather station of Uccle located in Belgium. A set of drought-related aspects is analysed, i.e. dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 28-member ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced by four future scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability in the climate. A performance comparison of the downscaling methods reveals that the QP method outperforms the others in reproducing the magnitude and monthly pattern of the observed indicators. While all methods show a good agreement on downscaling total precipitation, their results differ quite largely for the frequency and length of dry spells. Using the downscaling methods, dry day frequency is projected to increase significantly in the summer months, with a relative change of up to 19 % for SSP5–8.5. At the same time, total precipitation is projected to decrease significantly by up to 33 % in these months. Total precipitation also significantly increases in winter, as it is driven by a significant intensification of extreme precipitation rather than a dry day frequency change. Lastly, extreme dry spells are projected to increase in length by up to 9 %.

Impacts and uncertainties of climate change on stream flow of the Bilate River (Ethiopia), using a CMIP5 general circulation models ensemble

The impact and uncertainty of climate change on stream flow of the Bilate River Watershed was assessed. Ensemble of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) under two Representative Concentration Pathways and six GCM structures were selected to form 24 future climate scenarios for the watershed. Soil and Water Assessment Tool (SWAT) model was selected to simulate stream flow of the watershed. The respective statistical results of the coefficient of determination (R2), Nash-Sutcliffe coefficient (NSE) and percent bias (PB) are 0.79, 0.78 and 0.56 for calibration period and 0.64, 0.60 and -21.7 for validation period which show that the model predicted the stream flow reasonably. The annual stream flow increased progressively throughout the century for all time periods. The increases under RCP 8.5 scenario are the larger compared to RCP 4.5 scenarios, approximately 42.42% during the 2080s period. The six GCMs selected to see the uncertainties related to GCMs suggest that the river flow will change by small amounts of -6.18 to 7.83% change compared with the baseline. The simulated runoff depended on the projected amount of rainfall embedded in the GCM structures selected to simulate the future climate and less dependent on the local temperature increment. Key words: Climate change, Bilate River watershed, stream flow, soil and water assessment tool (SWAT), uncertainty.

Present and future trends in winds and SST off central East Africa

The coast of central East Africa (CEA) is a dynamic region in terms of climate, in which fisheries and marine-related services impact a large portion of the population. The main driver of regional dynamics is the seasonal alternation of the Northeast (NE) and Southeast (SE) monsoons. Winds associated with these monsoons modulate the prevalent, remotely-forced East African Coastal Current (EACC). Here, present and future trends in winds and sea surface temperature (SST) of the CEA and adjacent regions are investigated using reanalysis and reconstructed data, and an ensemble of General Circulation Models. It was found that the winds and SST show unidirectional trends, with magnitude and spatial differences between the NE and SE monsoons. Winds show weakening trends during the NE monsoon, in the past and future, of the Somali region; with no significant trends during the SE monsoon. SST shows increasing trends in the entire region in the past and future, with stronger warming during the NE monsoon off Somalia; SST trends are smaller in the CEA. These trends could impact the CEA through increased water-column stability and decreased upwelling due to shifting of the EACC separation from the continent. However, given the coarse resolution of data analyzed, regional modeling is still necessary to understand the impacts on local dynamics and productivity in the CEA.

Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

Decreasing wheat yield stability on the North China Plain: Relative contributions from climate change in mean and variability

AbstractThere has been increasing interest in understanding climate change impacts on crop yield stability, including interannual yield variability and lower yield extremes, in addition to mean yield. In this study, we evaluated these impacts on wheat yield and investigated the contribution of changes in climate mean and variability, and their interaction, on the North China Plain (NCP). Wheat yield simulation experiments with control groups were conducted using the Crop Environment Resource Synthesis (CERES) model, with multiple general circulation model ensembles under two representative concentration pathways (RCPs), namely 4.5 and 8.5. Climate change was projected to reduce mean yield by 15 and 17%, increase yield interannual variability by 5 and 11%, and reduce 10‐year return period lower yield extremes by 31 and 34% under RCPs 4.5 and 8.5, respectively. When analysed, changes in climate mean proved the main cause for changes in mean yield (62–71%), followed by the interactive changes in climate mean and variability (26–33%). As for the impact on yield variability, the interaction of the changes in climate mean and variability proved the main cause (48–54%), followed by changes in climate mean (33–41%). Surprisingly, climate change in variability contributed the least in both cases. Our results pertaining to the decrease in both availability and stability of wheat yield on the NCP presents a greater challenge for building a resilient food system for local areas than before. They also highlighted the importance of separating the impacts of changes in climate mean and variability on crop yield stability in a holistic framework, with particular attention paid to the tangible and interactive effects.

Multi-variable model output statistics downscaling for the projection of spatio-temporal changes in rainfall of Borneo Island

A multiple variable bias correction approach has been proposed for the projection of the changes in spatial and temporal pattern of rainfall in Borneo Island due to climate change. The ensemble of General Circulation Models (GCMs) was selected through the combination of past performance and envelope approaches. The selected GCMs were downscaled using a Support Vector Machine (SVM) based multiple variable bias correction approach considering that inclusion of multiple circulation variables can provide more information on local climate and able to explicitly account for GCM-inherent error and bias. Finally, an ensemble projection was produced by using Random Forest (RF) regression to simulate the future projection. Four GCMs namely, HadGEM2-AO, HadGEM2-ES, MIROC5 and CCSM4 were found most suitable for the projection of rainfall in Borneo with associated uncertainty. The projected changes in rainfall indicate that the rainfall pattern and variation in Borneo are dominated based on the changes of monsoon season, geographical locations and terrains of the region. Overall, there is an increasing rate of rainfall projections in most parts of Borneo. However, the coastal-western region will become drier in contrast to the eastern region, although rainfall variability remains high. The highest increase in rainfall was projected in East Kalimantan in the range of 11.9% to 50.1% for 2070–2099, while the highest decrease was projected in West Kalimantan in the range of −3.7% to −13.0% for 2010–2039. The rainfall was expected to be more distributed during the Southwest monsoon while shorter Northeast monsoon with higher intensity was projected at most regions.

Pseudo-climate modelling study on projected changes in extreme extratropical cyclones, storm waves and surges under CMIP5 multi-model ensemble: Baltic Sea perspective

In order to estimate the possible parameters of future extreme extratropical cyclones (ETCs), a pseudo-climate modelling study of three historical storms originating from the Atlantic Ocean and one from the Black Sea area was performed using multi-model approach considering IPCC emission scenarios RCP4.5 and RCP8.5 for the twenty-first century. Applying Weather Research and Forecasting atmosphere model (WRF), Finite Volume Community Ocean model (FVCOM-SWAVE) and the Simulating WAves Nearshore (SWAN) model, the changes in initial conditions in atmospheric air temperature, sea surface temperature and relative humidity were considered on the basis of 14 CMIP5 general circulation models ensemble. According to the future scenario results, no notable changes are expected in minimum atmospheric pressure within the ETCs of the future; however, the low pressure area was slightly larger and the strong wind zone was extending further south with greater peak wind speeds in the future (year 2081–2100) simulations. This, in turn, yielded a small surge height increase at Parnu under RCP4.5 scenario; however, under RCP8.5 scenario the surge increase was up to 22–59 cm. Westerly approaching ETCs will bring more precipitation to the Baltic Sea area in the (warmer) future. In case of a southerly cyclone, the results were more mixed. An insignificant increase in wave heights during extreme storm conditions occurred. Although RCP8.5 future scenario is usually considered as unrealistic, the results of this study still suggest that the extreme ETCs may become more dangerous in the future, although probably not as certainly as tropical cyclones.

The Optimal Multimodel Ensemble of Bias-Corrected CMIP5 Climate Models over China

AbstractA multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies.

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980–2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 °C under RCP4.5 and 1.25–1.79 °C under RCP8.5 during the periods 2070–2099. Tmin also revealed a significant increase of 0 to 0.52 °C under RCP4.5 and between 1.38–2.02 °C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures.