Multi-model Ensemble Projections Research Articles

Heat stress may cause a significant reduction of rice yield in China under future climate scenarios

Extreme heat events have become more frequent and severe under climate change and seriously threaten rice growth. Most existing crop models tend to underestimate the impacts of heat stress on rice yields. Heat stress modules in crop models have not been extensively explored, particularly on a large scale. This study modeled rice growth under heat stress at the flowering and filling stages through two heat stress models which coupled into the CERES-Rice model. We evaluated the advanced model with provincial statistics and Gridded Observed Rice Yield. Our improved CERES-Rice model produced more accurate estimates on rice yield than the original model evidenced by an increased correlation coefficient (R) of 12.72% and d-index of 0.02%. The RMSE and MAE decreased by 5.94% and 6.01%, respectively. Most pseudo positive correlations between rice yield and the number of heat days were corrected to the negative ones by the improved model. The future projections from the improved model signifies multi-model ensemble yield projection without CO2 effect (MME-I-NOCO2) has an apparent fall from 2020 to 2099 under RCP4.5, RCP6.0 and RCP8.5 with the decreasing percentages of 6%, 14%, and 37%, respectively, whereas the decreasing trend (12%) only occurs under RCP8.5 with CO2 effect (MME-I-CO2). The apparently decreasing trends of yield projection from MME-I-NOCO2 will occur in most rice-planted regions of China with the decreasing rate < 50 kg/ha/a especially in the central-south and southern cropping regions, and this decreasing trend will be slowed down for MME-I-CO2. Relative to rice yield of historical period, rice yield variations of MME-I-NOCO2 for different growing seasons show a downward trend with the decrease of approximately 54%, 60%, and 43%, respectively. Our study highlights the importance of modeling crop yields under heat stress to food security, agricultural adaptation and mitigation to climate change.

Projected changes in East African climate and its impacts on climatic suitability of maize production areas by the mid-twenty-first century.

Maize crop (Zea mays) is one of the staple foods in the East African (EA) region. However, the suitability of its production area is threatened by projected climate change. The Multimodel Ensemble (MME) from eight Coupled Model Intercomparison Project 5 (CMIP5) models was used in this paper to show climate change between the recent past (1970-2000) and the future (2041-2060), i.e., the mid-twenty-first century. The climatic suitability of maize crop production areas is evaluated based on these climate datasets and the current maize crop presence points using Maximum entropy models (MaxEnt). The MME projection showed a slight increase in precipitation under both RCP4.5 and RCP8.5 in certain places and a reduction in most of southern Tanzania. The temperature projection showed that the minimum temperature would increase by 0.3 to 2.95°C and 0.3 to 3.2°C under RCP4.5 and 8.5, respectively. Moreover, the maximum temperature would increase by 1.0 to 3.0°C and 1.2 to 3.6°C under RCP4.5 and 8.5 respectively. The impacts of these projected changes in climate on maize production areas are the reduction in the suitability of the crop, especially around central and western Tanzania, mid-northern and western Uganda, and parts of western Kenya by 20-40%, and patches of EA will experience a reduction of as high as 40-60%, especially in northern Uganda, and western Kenya. The projected changes in temperature and precipitation present a significant negative change in maize crop suitability. Thus, food security and the efforts towards the elimination of hunger in EA by the mid-twenty-first century will be hampered significantly. We recommend crop diversification to suit the new future environments, modernizing maize farming programs through the adoption of new technologies including irrigation, and climate-smart agricultural practices, etc.

Multi-model ensemble projections of soil moisture drought over North Africa and the Sahel region under 1.5, 2, and 3 °C global warming

The intensification of soil moisture drought events is an expected consequence of anthropogenic global warming. However, the implication of 1.5–3 °C global warming on these events remains unknown over North Africa and the Sahel region, where soil moisture plays a crucial role in food security that largely depends on rainfed agriculture. Here, using a multi-model ensemble from the Inter-Sectoral Impact Model Intercomparison Project phase 2b, we estimate the changes in the spatiotemporal characteristics of soil moisture drought events under increased global mean temperature. A 3 °C global warming results in multi-year (up to 19 years) mega-drought events over North Africa compared to a maximum drought duration of 12 years under the 1.5 °C Paris Agreement target. These events are projected to transform from historically severe droughts into exceptional droughts and extend over an area that is 32% larger under 3 °C compared to that under 1.5 °C. Global warming also leads to a high intensification of Sahelian drought extremes, in particular, their duration (from 24 to 82 months between 1.5 and 3 °C) over the western parts and their severity everywhere. Even though the results highlight substantial uncertainties arising from climate forcing and impact models, the projections indicate a tendency toward unprecedented exacerbation of soil moisture droughts that could pose serious threats to food security of North African and Sahelian societies in the absence of effective mitigation and adaptation.

Multimodel Ensemble Projections of Wave Climate in the Western North Pacific Using CMIP6 Marine Surface Winds

For decades, the western North Pacific (WNP) has been commonly indicated as a region with high vulnerability to oceanic and atmospheric hazards. This phenomenon can be observed through general circulation model (GCM) output from the Coupled Model Intercomparison Project (CMIP). The CMIP consists of a collection of ensemble data as well as marine surface winds for the projection of the wave climate. Wave climate projections based on the CMIP dataset are necessary for ocean studies, marine forecasts, and coastal development over the WNP region. Numerous studies with earlier phases of CMIP are abundant, but studies using CMIP6 as the recent dataset for wave projection is still limited. Thus, in this study, wave climate projections with WAVEWATCH III are conducted to investigate how wave characteristics in the WNP will have changed in 2050 and 2100 compared to those in 2000 with atmospheric forcings from CMIP6 marine surface winds. The wave model runs with a 0.5° × 0.5° spatial resolution in spherical coordinates and a 10-min time step. A total of eight GCMs from the CMIP6 dataset are used for the marine surface winds modelled over 3 h for 2050 and 2100. The simulated average wave characteristics for 2000 are validated with the ERA5 Reanalysis wave data showing good consistency. The wave characteristics in 2050 and 2100 show that significant decreases in wave height, a clockwise shift in wave direction, and the mean wave period becomes shorter relative to those in 2000.

Climate Change Analysis for Bangladesh Using CMIP5 Models

Using the Coupled Model Inter-comparison Project phase 5 (CMIP5) global climate modeling predictions, the study analyzes the distribution of rainfall and temperature in Bangladesh in recent years (1981–2005) and in three future periods (2025–2049, 2050–2074 and 2075–2099) considering RCP 4.5 and RCP 8.5 scenarios. In the historical period, all three CMIP5 models (MPI-ESM-LR, MPI-ESM-MR and NorESM1-M) mostly underestimated the observed mean rainfall data collected from Bangladesh Meteorological Department (BMD), but for temperature the result is found almost similar between models and observation. The CMIP5 models simulation reveal biases in monthly mean rainfall and temperature over Bangladesh in the past, so bias-correction is performed for future data. Quantile mapping bias-correction reduces significant amount of biases from the projection data of rainfall and temperature. By the end of the twenty-first century, the multi-model ensemble annual mean rainfall averaged over Bangladesh is projected to change between -2% to 9% and -3% to 15% under RCP 4.5 and RCP 8.5, respectively. The changes in spatial patterns of annual rainfall indicate a decrease in rainfall over a major portion in the west and the northwestern and an increase in the southeast, east, and the northeastern part of the country. The multi-model ensemble projection reveals a continuous increase in the annual mean temperature and shows a larger increase over the northwestern part and west-central part of Bangladesh. By the end of the twenty-first century, the annual mean temperature over Bangladesh is projected to increase by 1.9 (0.9-2.8) °C and 4 (2.8-4.6) °C under the RCP 4.5 and RCP 8.5 scenarios respectively, relative to the reference climate (1981-2005).&#x0D; The Dhaka University Journal of Earth and Environmental Sciences, Vol. 9(1), 2020, P 1-12

Assessment of meteorological drought change in the 21st century based on CMIP6 multi-model ensemble projections over mainland China

Understanding drought dynamics spatially and temporally is essential for environmental and socio-economic systems, especially under climate change with expected increasing drought. This study aims to update the knowledge about drought spatiotemporal changes over mainland China in the 21st century by utilizing the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. Precipitation from the nine CMIP6 models is first bias-corrected using a quantile mapping approach and then used to calculate the 3-month Standardized Precipitation Index (SPI) for the historical period and future three scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). We conduct a clustering algorithm to identify three-dimensional (i.e., latitude, longitude, and time) drought events to access the drought dynamics in both space and time. Results show that the drought events occur more frequently during June to September than the other months for both historical and future periods. The drought frequency is significantly increasing during Sep–Dec in the 21st century (increase by about 8.5%, p-value<0.05). Compared with the historical period, drought centers shift toward south under the future projections, indicating a relatively high drought frequency in south China. Additionally, drought events are projected to have shorter duration, smaller affected area, but higher intensity under all three scenarios. The study contributes to our comprehensive understanding of how future drought characteristics will change due to climate change from the perspective of spatiotemporal dynamics. These results are helpful to drought risk management and provide information for developing drought adaptation strategies.

An Empirical Seasonal Rainfall Forecasting Model for the Northeast Region of Brazil

The Northeast region of Brazil (NEB) is characterized by large climate variability that causes extreme and long unseasonal wet and dry periods. Despite significant model developments to improve seasonal forecasting for the NEB, the achievement of a satisfactory accuracy often remains a challenge, and forecasting methods aimed at reducing uncertainties regarding future climate are needed. In this work, we implement and assess the performance of an empirical model (EmpM) based on a decomposition of historical data into dominant modes of precipitation and seasonal forecast applied to the NEB domain. We analyzed the model’s performance for the February-March-April quarter and compared its results with forecasts based on data from the North American Multi-model Ensemble (NMME) project for the same period. We found that the first three leading precipitation modes obtained by empirical orthogonal functions (EOF) explained most of the rainfall variability for the season of interest. Thereby, this study focuses on them for the forecast evaluations. A teleconnection analysis shows that most of the variability in precipitation comes from sea surface temperature (SST) anomalies in various areas of the Pacific and the tropical Atlantic. The modes exhibit different spatial patterns across the NEB, with the first being concentrated in the northern half of the region and presenting remarkable associations with the El Niño-Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), both linked to the latitudinal migration of the intertropical convergence zone (ITCZ). As for the second mode, the correlations with oceanic regions and its loading pattern point to the influence of the incursion of frontal systems in the southern NEB. The time series of the third mode implies the influence of a lower frequency mode of variability, probably related to the Interdecadal Pacific Oscillation (IPO). The teleconnection patterns found in the analysis allowed for a reliable forecast of the time series of each mode, which, combined, result in the final rainfall prediction outputted by the model. Overall, the EmpM outperformed the post-processed NMME for most of the NEB, except for some areas along the northern region, where the NMME showed superiority.

Summer heat sources changes over the Tibetan Plateau in CMIP6 models

The elevated summer heat sources over the Tibetan Plateau (TP) profoundly influence Asian monsoon and atmospheric general circulation. Model simulations and future changes of condensational latent heat released from precipitation and surface sensible heat (SH) over the eastern TP are investigated with 22 CMIP6 models’ outputs. The models reproduce the mean precipitation pattern well, but the mean intensity is 65% excessive. The SH has scarcely been evaluated. We find that nearly half of the models cannot realistically capture the SH’s spatial structure. The best six models in simulating the SH are the same models that best simulate surface air temperature. The models with high performance are selected to make a multi-model ensemble mean projection. Under the medium emission scenario (SSP2-4.5), the TP’s future summer precipitation will likely increase, despite its weakening thermal forcing effect. The increasing precipitation is primarily due to the future enhancement in vertical moisture transport and surface evaporation. However, the greenhouse gases-induced top-heavy heating stabilizes the atmosphere and diminishes the TP’s thermal forcing effect, weakening the circulation and upward motion. As such, the precipitation sensitivity is only a 2.7% increase per degree Celsius global warming. The projected SH will be likely unchanged in accord with the likely unaltered surface wind speed. These results have important implications for the future change of the water supplies in the heavily populated South and East Asian countries. They could help the modeling groups further improve the climate model performance in the highland regions.

Model-wise uncertainty decomposition in multi-model ensemble hydrological projections

Model-wise uncertainty decomposition in multi-model ensemble hydrological projections

Projected changes in temperature and precipitation over mainland Southeast Asia by CMIP6 models

AbstractFive mainland SEA countries (Cambodia, Laos, Myanmar, Vietnam, and Thailand) are threatened by climate change. Here, the latest 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) is employed to examine future climate change in this region under two SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The bias-corrected multi-model ensemble (MME) projects a warming (wetting) over Cambodia, Laos, Myanmar, Vietnam, and Thailand by 1.88–3.89, 2.04–4.22, 1.88–4.09, 2.03–4.25, and 1.90–3.96 °C (8.76–20.47, 12.69–21.10, 9.54–21.10, 13.47–22.12, and 7.03–15.17%) in the 21st century with larger values found under SSP5-8.5 than SSP2-4.5. The MME model displays approximately triple the current rainfall during the boreal summer. Overall, there are robust increases in rainfall during the Southwest Monsoon (3.41–3.44, 8.44–9.53, and 10.89–17.59%) and the Northeast Monsoon (−2.58 to 0.78, −0.43 to 2.81, and 2.32 to 5.45%). The effectiveness of anticipated climate change mitigation and adaptation strategies under SSP2-4.5 results in slowing down the warming trends and decreasing precipitation trends after 2050. All these findings imply that member countries of mainland SEA need to prepare for appropriate adaptation measures in response to the changing climate.

Evaluation and projection of mean surface temperature using CMIP6 models over East Africa

This study evaluates the historical mean surface temperature (hereafter T2m) and examines how T2m changes over East Africa (EA) in the 21st century using CMIP6 models. An evaluation was conducted based on mean state, trends, and statistical metrics (Bias, Correlation Coefficient, Root Mean Square Difference, and Taylor skill score). For projections over EA, five best performing CMIP6 models (based on their performance ranking in historical mean temperature simulations) under the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 scenarios were employed. The historical simulations reveal an overestimation of the mean annual T2m cycle over the study region with fewer models depicting underestimations. Further, CMIP6 models reproduce the spatial and temporal trends within the observed range proximity. Overall, the best performing models are as follows: FGOALS-g3, HadGEM-GC31-LL, MPI-ESM2-LR, CNRM-CM6-1,andIPSL-CM6A-LR. During the three-time slices under consideration, the Multi Model Ensemble (MME) project many changes during the late period (2080–2100) with expected mean changes at 2.4 °C for SSP2-4.5 and 4.4 °C for the SSP5-8.5 scenario. The magnitude of change based on Sen's slope estimator and Mann-Kendall test reveal significant increasing tendencies with projections of 0.24 °C decade-1 (0.65 °C decade-1) under SSP2-4.5(SSP5-8.5) scenarios. The findings from this study illustrate higher warming in the latest model outputs of CMIP6 relative to its predecessor, despite identical instantaneous radiative forcing.

Climate change impacts on pistachio cultivation areas in Iran: a simulation analysis based on CORDEX-MENA multi-model ensembles

Climate and especially air temperature have a direct and high impact on nut trees in arid regions. In this regard, the use of multi-model ensemble projections as more reliable models is important. In this study, based on multi-model ensembles (MME) from the coordinated regional climate downscaling experiment Middle East North Africa (CORDEX-MENA), the air temperature in RCP8.5 and RCP4.5 pathways in pistachio cultivation areas in Iran was projected. The period 1980 to 2017 was identified as the reference period. Due to the sensitivity of climate change and the existing uncertainties, the hourly temperatures based on the models of chilling hours (CH) and chilling portions (CP) were also used for the reference period. The results of CORDEX simulations for pistachio cultivation areas suggest that the minimum and maximum annual air temperature (MMAT) and growing season temperature (GST) at the end of the current century compared to 1980–2017 as the reference period will increase significantly to 4 °C. The downward trend of winter chill accumulation during the cold season based on observational data will confirm the increase in air temperature in pistachio cultivation areas in Iran. In the output simulation of CORDEX project, the date of flowering pistachio tree for late-maturing varieties by 2080 under representative concentration pathway (RCPs) RCP8.5 and RCP4.5 will occur 16 days and 6 days sooner, respectively. The amount of heat accumulation or growing degree days (GDD) in the future in pistachio cultivation areas will be increased, and deviation from the historical period, based on RCP8.5 and RCP4.5 on average, will occur 3200 and 2600 GDD, respectively.

Copula-based risk evaluation of global meteorological drought in the 21st century based on CMIP5 multi-model ensemble projections

Climate warming is expected to have significant impacts on the global hydrologic cycle, including changes in precipitation-induced extremes such as droughts. Using 28 CMIP5 global climate models (GCMs), this study presents a global-scale analysis of the joint return periods (T) of meteorological drought characteristics (duration D, severity S, and peak P) at the 6- and 12-month scales under the Representative Concentration Pathways scenarios RCP2.6 and RCP4.5. The D, S, and P estimated based on the Runs theory are used to calculated from the Standardized Precipitation Index (SPI) at the global 1° × 1° grids. Six marginal probability distributions are used to fit S, P and D, whereas three Archimedean copula functions (Clayton, GH and Frank) are used to estimate T for the paired drought characteristics (S-D, P-D and S-P). Large spatial variability is found globally in the best-fitted copulas for the paired drought characteristics, with the Frank (Frank and GH) of the largest global percentage for SPI6 (SPI12). Relative to the baseline (1971–2000), the T of the paired drought characteristics above the moderate drought (S > 1, P > 1 and D > 3) is projected to decrease mostly in North America and Asia (<0.22-year) during the 2021–2050 near-future (NF) period, and in South America and Australia (<0.47-year) during the 2071–2100 far-future (FF) period. In contrast, an increase in T is projected during NF (<3-year) mostly in South America and Australia, and during FF (<49-year) mostly in Asia, Europe and North America. Furthermore, a larger increase in T is projected under higher RCP and at longer timescales. Our results suggest a generally nonlinear response of the projected drought risk changes to anthropogenic forcing during the 21st century.

Multi-model ensemble projection of mean and extreme streamflow of Brahmaputra River Basin under the impact of climate change

Abstract The streamflow of Brahmaputra River Basin is vital for sustainable socioeconomic development of the Ganges delta. Frequent floods and droughts in the past decades indicate the susceptibility of the region to climate variability. Although there are multiple studies investigating the basin's future water availability, most of those are based on limited climate change scenarios despite the wide range of uncertainties in different climate model projections. This study aims to provide a better estimation of projected future streamflow for a combination of 18 climate change scenarios. We develop a hydrologic model of the basin and simulate the future water availability based on these climate change scenarios. Our results show that the simulated mean annual, mean seasonal and annual maximum streamflow of the basin is expected to increase in future. By the end of the 21st century, the projected increase in mean annual, mean dry season, mean wet season, and annual maximum streamflow is about 25, 178, 11, and 22%, respectively. We also demonstrate that this projected streamflow can be expressed as a multivariate linear regression of projected changes in temperature and precipitation in the basin and would be very useful for policy makers to make informed decision regarding climate change adaptation.

Multi-Model Ensemble Projection of Precipitation Changes over China under Global Warming of 1.5 and 2°C with Consideration of Model Performance and Independence

A weighting scheme jointly considering model performance and independence (PI-based weighting scheme) is employed to deal with multi-model ensemble prediction of precipitation over China from 17 global climate models. Four precipitation metrics on mean and extremes are used to evaluate the model performance and independence. The PI-based scheme is also compared with a rank-based weighting scheme and the simple arithmetic mean (AM) scheme. It is shown that the PI-based scheme achieves notable improvements in western China, with biases decreasing for all parameters. However, improvements are small and almost insignificant in eastern China. After calibration and validation, the scheme is used for future precipitation projection under the 1.5 and 2°C global warming targets (above preindustrial level). There is a general tendency to wetness for most regions in China, especially in terms of extreme precipitation. The PI scheme shows larger inhomogeneity in spatial distribution. For the total precipitation PRCPTOT (95th percentile extreme precipitation R95P), the land fraction for a change larger than 10% (20%) is 22.8% (53.4%) in PI, while 13.3% (36.8%) in AM, under 2°C global warming. Most noticeable increase exists in central and east parts of western China.

Multi-model ensemble projections of extreme ocean wave heights over the Indian ocean

Extreme ocean waves can have devastating impacts on many populous coastal regions or offshore islands. Yet, knowledge of how ocean waves are likely to respond to future climate change remains limited. To assess potential increases in risk associated with extreme ocean waves, future changes in seasonal mean and extreme significant wave height (SWH) are examined over the Indian Ocean (IO) using 18 Coupled Model Intercomparison Project Phase 5 (CMIP5) models forced with representative concentration pathway (RCP) 4.5 and 8.5 scenarios. The seasonal maxima are fit to the generalized extreme value (GEV) distribution and corresponding 10-year return values are estimated for the present-day (1981–2010) and future periods (2070–2099). Overall, projected changes in IO SWH exhibit noticeable seasonality. Under the high emissions RCP8.5 scenarios, mean and extreme SWH in the Arabian Sea (AS) and Bay of Bengal (BOB) are projected to increase during all seasons except December–February (DJF). In the western tropical IO (TIO), mean and extreme SWHs are projected to increase during June–August (JJA) and September–November (SON) in line with the projected circulation changes toward an Indian Ocean Dipole (IOD) positive phase-like mean state. Southern IO (SIO) SWHs exhibit a strong zonal shift, with large increases over high-latitudes and decreases over mid-latitudes, which is related to future changes in the Southern Annular Mode (SAM) toward its positive phase. Interestingly, some regions like the western TIO show significantly less increases in SWH under the lower emissions RCP4.5 scenarios, highlighting avoidable future risk through global warming mitigation efforts.

Impact of the tropical Pacific SST biases on the simulation and prediction of Indian summer monsoon rainfall in CFSv2, ECMWF-System4, and NMME models

Present study analyses the role of tropical Pacific sea surface temperature (SST) biases in simulating the mean and inter-annual variability of Indian summer monsoon rainfall (ISMR) using the hindcasts from Monsoon Mission CFSv2 (MMCFS) and ECMWF-SYSTEM4 (ES4) prediction systems. ES4 simulates the mean and annual cycle of ISMR better than MMCFS when initialized with February initial conditions (3-month lead, Feb IC). At the same time, interannual variability (IAV) and skill (correlation between the ensemble mean hindcast and observations) are the highest (least) for MMCFS (ES4). May IC (0-month lead) hindcasts of both the models have similar mean, annual cycle, IAV and skill. Further analysis shows that ES4_Feb IC hindcasts exhibit a strong cold bias (~ 2 °C) in the equatorial central Pacific, which is close to zero for MMCFS_Feb IC. Meanwhile, the 0-month lead (May IC) hindcast has a similar cold bias (~ 1.5 °C) in the equatorial Pacific for both the model hindcasts. Thus, model hindcasts with a better mean ISMR are having a strong cold bias in the equatorial Pacific and that having very less SST bias has higher IAV and skill for ISMR simulations. Additional investigation using North American National Multi-Model Ensemble Project (NMME) models is carried out and the following conclusion on the different roles of Pacific mean state bias is drawn. Hindcasts with strong cold SST bias in the tropical Pacific (ES4 hindcast and May IC hindcasts of MMCFS) tend to mimic the teleconnections associated with La Nina conditions reducing the dry bias over India, resulting in mean ISMR closer to the observed value, still less than the observed mean. At the same time, due to the same strong cold bias, the El Nino-Southern Oscillation (ENSO) induced rainfall and circulation pattern in the Pacific are weak and extended further northwestward. This weakens the ENSO induced heat sources over the Indian Ocean and monsoon region, resulting in reduced ENSO-ISMR teleconnections. These factors result in a poor simulation of interannual variability and skill of ISMR. On the other hand, model hindcasts with less cold bias in the equatorial Pacific (MMCFS_Feb IC) suffer from strong dry bias over India; however, the skill of ISMR is higher as a result of strong ENSO-Monsoon teleconnections. Thus the study confirms the differential role of SST bias in the tropical Pacific in simulating mean and IAV of ISMR in seasonal prediction models. A close to observed mean SST in the tropical Pacific and proper ENSO-Monsoon teleconnection is essential for the better skill of ISMR in the present generation seasonal prediction models.

On the use of convolutional Gaussian processes to improve the seasonal forecasting of precipitation and temperature

This study examines the potential improvement in seasonal predictability of monthly precipitation and temperature using a novel machine learning approach, the convolutional Gaussian process (CGP). This approach allows us to take into account multiple quantities and their interdependencies simultaneously. We use one global climate model (FLORb01) part of the North American Multi-Model Ensemble (NMME) project and quantify its skill in reproducing precipitation and temperature in March and July across Iowa (central United States) for lead times from one month to one year. As a first step we train the CGP over the 1985–2005 period, and then apply it out of sample from 2006 to 2019. Over the validation period, our results indicate that the CGP is able to increase the skill (i.e., increased correlation coefficient and reduced root mean squared error) in predicting precipitation and temperature compared to both the raw outputs and after standard bias correction. These statements are consistent across different lead times and target month (i.e., March or July). These encouraging findings provide a new potential path towards improved predictability of the regional climate at the seasonal scale.

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.

Comparison of Ensembles Projections of Rainfall from Four Bias Correction Methods over Nigeria

This study compares multi model ensemble (MME) projections of rainfall using general quantile mapping, gamma quantile mapping, Power Transformation and Linear Scaling bias correction (BC) methods for representative concentration pathways (RCPs) 4.5 and 8.5 of the Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). Using the Global Precipitation Climatology Centre historical period (1961–2005) rainfall data as the reference, projection was conducted over 323 grid points of Nigeria for the periods 2010–2039, 2040–2069 and 2070–2099. The performances of the different BC methods in removing biases from the GCMs were assessed using different statistical indices. The computation of the MME of the projected rainfall was conducted by aggregation of 20 GCMs using random forest regression method. The percentage differences in the future rainfall relative to the historical period were estimated for all BC methods. Spatial projection of the percentage changes in rainfall for Linear scaling, which was the best performing BC method, showed increases in rainfall of 5.5–6.9% under RCPs 4.5 and 8.5, respectively, while the decrease range was −3.2–−4.2% respectively during the wet season. The range of annual increases in precipitation was 5.7–7.3% for RCP 4.5 and 8.5, respectively, while the decrease range was −1.0–−4.3%. This study also revealed monthly rainfall within the country will decrease during the wet season between June and September, which is a significant period where most crops need the water for growth. Findings from this study can be of importance to policy makers in the management of changes in hydrological processes due to climate change and management of related disasters such as floods and droughts.