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Future soil moisture (SM) estimation is a practically useful task for eco-hydrologists, agriculturists, and stakeholders in environment health monitoring to generate comprehensive understanding of hydro-physical and soil dynamic system. This paper demonstrates the capability of a hybridised long short-term memory (LSTM) predictive framework to emulate SM under global warming scenarios. The proposed model is developed by integrating Boruta-random forest (BRF) feature selection and capturing significant antecedent memory of SM behaviour were applied to estimate the future SM using Coupled Model Intercomparison Phase-5 (CMIP5) repository. The BRF is adapted to extract pertinent features in hydro-meteorological variables intrinsically related to SM, and therefore, is used to construct a hybridised deep learning (i.e., BRF-LSTM) model. To establish the viability of deep learning model for SM estimation until 2100, five stations closely matched to the global climate model grid are selected in Australia's Murray Darling Basin. The performance skill of BRF-LSTM model is compared against standalone models (i.e., LSTM, SVR, and MARS). The results showed that the hybrid deep learning model (i.e., BRF-LSTM) with a feature selection capability could significantly outperform the standalone models for both warming simulations. The proposed hybrid model also demonstrated superiority in SM estimation with over 95% of all predictive errors lying below 0.02 mm, and low relative root means square error (≈ 1.06% for RCP4.5 and ≈ 1.888% for RCP8.5) to outperform all the benchmark models. This study demonstrates the capability of LSTM algorithm coupled with BRF feature selection to simulate future soil moisture under climate change, and so, can be successfully implemented in hydrology, agriculture, soil use management and environmental management.

Future disruptions to fire activity will threaten ecosystems and human well-being throughout the world, yet there are few fire projections at global scales and almost none from a broad range of global climate models (GCMs). Here we integrate global fire datasets and environmental covariates to build spatial statistical models of fire probability at a 0.5° resolution and examine environmental controls on fire activity. Fire models are driven by climate norms from 16 GCMs (A2 emissions scenario) to assess the magnitude and direction of change over two time periods, 2010-2039 and 2070-2099. From the ensemble results, we identify areas of consensus for increases or decreases in fire activity, as well as areas where GCMs disagree. Although certain biomes are sensitive to constraints on biomass productivity and others to atmospheric conditions promoting combustion, substantial and rapid shifts are projected for future fire activity across vast portions of the globe. In the near term, the most consistent increases in fire activity occur in biomes with already somewhat warm climates; decreases are less pronounced and concentrated primarily in a few tropical and subtropical biomes. However, models do not agree on the direction of near-term changes across more than 50% of terrestrial lands, highlighting major uncertainties in the next few decades. By the end of the century, the magnitude and the agreement in direction of change are projected to increase substantially. Most far-term model agreement on increasing fire probabilities (∼62%) occurs at mid- to high-latitudes, while agreement on decreasing probabilities (∼20%) is mainly in the tropics. Although our global models demonstrate that long-term environmental norms are very successful at capturing chronic fire probability patterns, future work is necessary to assess how much more explanatory power would be added through interannual variation in climate variables. This study provides a first examination of global disruptions to fire activity using an empirically based statistical framework and a multi-model ensemble of GCM projections, an important step toward assessing fire-related vulnerabilities to humans and the ecosystems upon which they depend.

An unresolved problem of the current Global Climate Models (GCM) is the unrealistic distribution of rainfall over the Indian Summer Monsoon (ISM) region, which is also related to the persistent dry bias over the Indian landmass. Therefore, quantitative prediction of the intensity of rainfall events has remained a challenge for state-of-the-art GCMs. Based on observations, it is hypothesized that the insufficient growth of cloud droplets and the processes responsible for the cloud-to-rainwater conversion are the key components in distinguishing between shallow and convective clouds. The Eulerian–Lagrangian particle-by-particle-based small-scale model provides a path for reassessing the ‘autoconversion’ parameterization schemes and suggests the relative dispersion-based ‘autoconversion’ parameterization scheme for the climate model. The realistic information on cloud drop size distribution is incorporated into the microphysical parameterization scheme of the climate model. Two sensitivity simulations are conducted using the climate forecast system (CFSv2) model. The coupled climate model incorporates a relative dispersion-based Liu–Daum-type autoconversion parameterization scheme in place of the traditional Sundqvist-type autoconversion, which, based on small-scale model analysis, makes the model more accurate in simulating the probability distribution (PDF) of rainfall with accompanying specific humidity, liquid water content, and outgoing long-wave radiation (OLR). The improved simulation of rainfall PDF appears to have been aided by a significantly improved simulation of OLR, which led to a more accurate simulation of the ISM rainfall.

In coupled perturbed parameter ensemble (PPE) experiments or for development of a single coupled global climate model (GCM) in general, models can exhibit a slowdown in the Atlantic Meridional Overturning Circulation (AMOC) that can result in unrealistically reduced transport of heat and other tracers. Here we propose a method that researchers running PPE experiments can apply to their own PPE to diagnose what controls the AMOC strength in their model and make predictions thereof. As an example, using data from a 25-member coupled PPE experiment performed with HadGEM3-GC3.05, we found four predictors based on surface heat and freshwater fluxes in four critical regions from the initial decade of the spinup phase that could accurately predict the AMOC transport in the later stage of the experiment. The method, to our knowledge, is novel in that it separates the effects of the drivers of AMOC change from the effects of the changed AMOC. The identified drivers are shown to be physically credible in that the PPE members exhibiting AMOC weakening possess some combination of the following characteristics: warmer ocean in the North Atlantic Subpolar Gyre, fresher Arctic and Tropical North Atlantic Oceans and larger runoff from the Amazon and Orinoco Rivers. These characteristics were further traced to regional responses in atmosphere-only experiments. This study suggests promising potential for early stopping rules for parameter perturbations that could end up with an unrealistically weak AMOC, saving valuable computational resources. Some of the four drivers are likely to be relevant to other climate models so this study is of interest to model developers who do not have a PPE.

A regional climate model called WRF (Weather Research and Forecasting) was set up in a two-way, three-domain nested framework to simulate future May to August precipitation of central Alberta, Canada. WRF is forced with climate outputs from four Global Climate Models (GCMs) for the baseline period 1980–2005, and for 2041–2100 based on the Representative Concentration Pathways (RCP) 4.5 and 8.5 climate scenarios of the Intergovernmental Panel on Climate Change (IPCC). A quantile–quantile bias correction method and a regional frequency analysis were applied to acquire future grid-based IDF curves for the city of Edmonton. Future trends of air temperature and convective available potential energy (CAPE) are investigated. Future IDF curves are expected to have higher intensities because of projected higher air temperature and atmospheric water vapor, and projected increase in CAPE by 2071–2100. Our results likely mean that under the impact of climate change, the future risk of flooding in Edmonton would increase.

Performance of the latest high-resolution COordinated Regional Climate Downscaling EXperiment-Coordinated Output for Regional Evaluation (CORDEX-CORE) model experiment suites for simulating temperature over Northeast India (NEI) during 1979-2005 are assessed. Three different suites of Regional Climate Models (RCMs), i.e., COSMO, RegCM4.7 and REMO suites dynamically downscaled from three different Global Circulation Models (GCMs) available over the CORDEX South Asia domain are considered. The three RCMs are evaluated to assess the performance in simulating the spatial pattern of temperature over the study area. The model experiments could simulate pre- and post-monsoon temperatures fairly well as compared to monsoon and winter seasons. The RCMs show a higher positive correlation coefficient (CC) of 0.9 – 0.98. Over the majority of NEI, the added value (AV) and Brier skill score (BSS) exhibit positive values of 0.4-0.8 and 0.2-0.8 respectively, indicating additional information and/or improvement after downscaling. The inter-comparisons show that the present-day temperature over the study region is better captured in the ensemble than in the individual model. Individually, the MPI_LR_COSMO model better simulates the spatial pattern of temperature with a higher spatial correlation of ~ 0.956 than the other RCMs. The temperature extremes are also well represented spatially by the model. Overall, the COSMO model experiment suites were identified to be the best with the corresponding observation across the year over NEI.

There is a lack in representation of biosphere–atmosphere interactions in current climate models. To fill this gap, one may introduce vegetation dynamics in surface transfer schemes or couple global climate models (GCMs) with vegetation dynamics models. As these vegetation dynamics models were not designed to be included in GCMs, how are the latest generation dynamic global vegetation models (DGVMs) suitable for use in global climate studies? This paper reviews the latest developments in DGVM modelling as well as the development of DGVM–GCM coupling in the framework of global climate studies. Limitations of DGVM and coupling are shown and the challenges of these methods are highlighted. During the last decade, DGVMs underwent major changes in the representation of physical and biogeochemical mechanisms such as photosynthesis and respiration processes as well as in the representation of regional properties of vegetation. However, several limitations such as carbon and nitrogen cycles, competition, land-use and land-use changes, and disturbances have been identified. In addition, recent advances in model coupling techniques allow the simulation of the vegetation–atmosphere interactions in GCMs with the help of DGVMs. Though DGVMs represent a good alternative to investigate vegetation–atmosphere interactions at a large scale, some weaknesses in evaluation methodology and model design need to be further investigated to improve the results.

Climate change has altered global hydrological cycles mainly due to changes in temperature and precipitation, which may exacerbate the global and regional water shortage issues, especially in the countries along the Belt and Road (B&R). In this paper, we assessed water supply, demand, and stress under three climate change scenarios in the major countries along the Belt and Road. We ensembled ten Global Climate Model (GCM) runoff data and downscaled it to a finer resolution of 0.1° × 0.1° by the random forest model. Our results showed that the GCM runoff was highly correlated with the FAO renewable water resources and thus could be used to estimate water supply. Climate change would increase water supply by 4.85%, 5.18%, 8.16% and water demand by 1.45%, 1.68%, 2.36% under RCP 2.6, 4.5, and 8.5 scenarios by 2050s, respectively. As a result, climate change will, in general, have little impact on water stress in the B&R countries as a whole. However, climate change will make future water resources more unevenly distributed among the B&R countries and regions, exacerbating water stress in some countries, especially in Central Asia and West Asia. Our results are informative for water resource managers and policymakers in the B&R countries to make sustainable water management strategies under future climate change.

The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020–2039 (near future), 2040–2069 (mid-century), and 2080–2099 (end-of-the-century), relative to the baseline period (1995–2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region’s climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models’ outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region.

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.