Increased occurrence of high impact compound events under climate change

While compound weather and climate events (CEs) can lead to significant socioeconomic consequences, their response to climate change is mostly unexplored. We report the first multi-model assessment of future changes in return periods for the co-occurrence of heatwaves and drought, and extreme winds and precipitation based on the Coupled Model Intercomparison Project (CMIP6) and three emission scenarios. Extreme winds and precipitation CEs occur more frequently in many regions, particularly under higher emissions. Heatwaves and drought occur more frequently everywhere under all emission scenarios examined. For each CMIP6 model, we derive a skill score for simulating CEs. Models with higher skill in simulating historical CEs project smaller increases in the number of heatwaves and drought in Eurasia, but larger numbers of strong winds and heavy precipitation CEs everywhere for all emission scenarios. This result is partly masked if the whole CMIP6 ensemble is used, pointing to the considerable value in further improvements in climate models.

Decarbonization pathways for the residential sector in the United States

Residential GHG emissions in the United States are driven in part by a housing stock where on-site fossil combustion is common, home sizes are large by international standards, energy efficiency potential is large, and electricity generation in many regions is GHG-intensive. In this analysis we assess decarbonization pathways for the United States residential sector to 2060, through 108 scenarios describing housing stock evolution, new housing characteristics, renovation levels, and clean electricity. The lowest emission scenarios rely on very rapid decarbonization of electricity supply alongside extensive renovations to existing homes—focused on improving thermal envelopes and heat pump electrification of heating. Reducing the size, increasing the multifamily share, and increasing the electrification of new homes provide further emission cuts, and combining all strategies enables emissions reductions of 91% between 2020 and 2050. Construction becomes the main source of emissions in the most ambitious scenarios, motivating increased attention on reducing embodied emissions.

A statistical bias correction technique for global climate model predicted near-surface temperature in India using the generalized regression neural network

Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network – a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951–2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM modelled output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006–2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.

Effects of Evaporative Emissions Control Measurements on Ozone Concentrations in Brazil

In this work, the possible benefits obtained due to the implementation of evaporative emissions control measures, originating from vehicle fueling processes, on ozone concentrations are verified. The measures studied are: (1) control at the moment when the tank trucks supply the fuel to the gas stations (Stage 1); (2) control at the moment when the vehicles are refueled at the gas stations, through a device installed in the pumps (Stage 2); (3) same as the previous control, but through a device installed in the vehicles (ORVR). The effects of these procedures were analyzed using numerical modeling with the VEIN and WRF/Chem models for a base case in 2018 and different emission scenarios, both in 2018 and 2031. The results obtained for 2018 show that the implementation of Stages 1 and 2 would reduce HCNM emissions by 47.96%, with a consequent reduction of 19.9% in the average concentrations of tropospheric ozone. For 2031, the greatest reductions in ozone concentrations were obtained with the scenario without ORVR, and with Stage 1 and Stage 2 (64.65% reduction in HCNM emissions and 31.93% in ozone), followed by the scenario with ORVR and with Stage 1 and Stage 2 (64.39% reduction in HCNM emissions and 32.98% in ozone concentrations).

Impacts of Climate Change and Variability on Precipitation and Maximum Flows in Devil’s Creek, Tacna, Peru

Global projections of climate change indicate negative impacts on hydrological systems, with significant changes in precipitation and temperature in many parts of the world. As a result, floods and droughts are expected. This article discusses the potential effects of climate change and variability on the maximum precipitation, temperature, and hydrological regime in Devil’s Creek, Tacna, Peru. The outputs of precipitation and daily temperature of fifteen regional climate models were used for the RCP4.5 and RCP8.5 emission scenarios. The methodology used includes the bias correction and downscaling of meteorological variables using the quintiles mapping technique, hydrological modeling, the evaluation of two emission scenarios, and its effect on the maximum flows of the stream. The results of the multi-model ensemble show that the maximum annual precipitation will probably increase by more than 30% for the RCP4.5 and RCP8.5 scenarios for the 2021–2050 period relative to the 1981–2005 period. Likewise, as expected, the maximum flows could increase by 220% and 154% for the RCP4.5 scenarios for the 2021–2050 and 2051–2080 terms, respectively, and 234% and 484% for the RCP8.5 scenarios and for the 2021–2050 and 2051–2080 terms, respectively, concerning the recorded historical value, increasing the probability of flood events and damage in populations located downstream.

Present and Future Land Suitability Analysis for Almond and Pistachio Crops in the Beira Baixa Region Using Spatial Multicriteria Decision Systems

The objective of this study is to determine the suitability for the cultivation of emerging fruit crops in the Beira Baixa region. The suitability was examined for the present time and in the face of two future emission scenarios (RCP 4.5 and 8.5). For this purpose, the biophysical criteria determining the cultivation of pistachio tree and almond tree were processed using a G. The analysis was performed by the AHP. After dividing the problem into hierarchical levels of decision making, a pairwise comparison of criteria was performed to evaluate the weights of these criteria, based on a scale of importance. In the present conditions, about 16.4% of the study area is classified as highly suitable for almond tree and 15.9% to pistachio tree. For the future scenarios, the area with high suitability will increase both for almond tree and pistachio tree. The AHP was adequate in the evaluation of the emerging fruit tree species suitability, since it allowed the integration of the several criteria studied, being a useful tool, which allows the decision making and the resolution of problems.

Reviewing the elements of marine ice cliff instability

Antarctica’s ice sheets are the largest potential sea-level rise contributors, but projections of future sea-level rise yield wide ranges of estimates under different emission scenarios. An important factor in the variability of estimates is marine ice cliff instability (MICI). Inclusion of MICI yields the highest potential sea-level rise cases but also the largest uncertainty due to poor understanding of the factors that control it and the mechanisms of how it happens. Although evidence for MICI has been implied by paleo-ice sheet studies and observations of keel plough mark on sea-floor, recent statistical and modelling studies have suggested a lower magnitude of MICI effect on sea-level rise due to thinning of ice sheets and buttressing forces exerted on potentially failing cliffs. This paper reviews the factors that control MICI with the goal of identifying priorities for modern ice sheet studies to better bound the estimates.

Variability of Rainfall Over the Coastal Area In Bangladesh by Using Climate Model

The study attempt to understand the variability of rainfall by looking into the previous and future climate of the coastal area in Bangladesh from 1850 to 2100 by using the climate model (CMCC-CM- the Centro Euro-Mediterraneo Sui Cambiamenti Climatici Climate Model) of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). Rainfall data were collected from CMCC-CM by R programming for two GHGs emission scenarios (RCP 4.5 and RCP 8.5) referred to as ‘Representative Concentration Pathways (RCPs)’. The analysis has been conducted based on four seasons and an annual basis by plotting model data in MS Excel and R programming. The model shows that the average annual rainfall will increase from 1055.6 mm (during 1850-1900) to 1368.1mm (during 2051-2100) for RCP 4.5 while it will reach 1569.7mm (during 2050-2100) for RCP 8.5. Rainfall is also increasing for all seasons except winter. In winter season, the average rainfall will increase from 35.37mm (during 1850-1900) to 41.75mm (during 2051-2100) for RCP 4.5, where it will decrease from 35.37mm (during 1850-1900) to 22.55mm (during 2051-2100) for RCP 8.5 in the study area. The increasing and decreasing trend are more in high GHGs emission scenarios than in the medium, which will be alarming. Accordingly, this projection will be helpful to understand the adverse impacts of climatic elements and take short and long-term planning of decision-makers in that area. J. Asiat. Soc. Bangladesh, Sci. 47(2): 149-160, December 2021

The Study of Drought in Future Climate Scenarios in the Huang-Huai-Hai Region

In the context of global warming, agricultural production and social and economic development are significantly affected by drought. The future change of climate conditions is uncertain; thus, it is of great importance to clarify the aspects of drought in order to define local and regional drought adaptation strategies. In this study, the meteorological data from 1976 to 2005 was used as a historical reference, and nine Global Climate Models (GCMs), downscaling to meteorological stations from 2039 to 2089, were used as future climate data. Based on Penman–Monteith, the reference crop Evapotranspiration (ET0) and Standardized Precipitation Evapotranspiration Index (SPEI) of the reference crop in three emission scenarios of RCP2.6, RCP4.5, and RCP8.5, under future climate conditions, were calculated. A non-parameter Mann–Kendall trend test was performed on temperature, precipitation, ET0, and SPEI to analyze the drought spatiotemporal distribution traits under upcoming climate scenarios. The results showed that, under future climate conditions, SPEI values in most areas of the Huang-Huai-Hai region would continuously increase year by year, and drought would be alleviated to some extent at the same pace. However, with the increase of greenhouse gas concentration in the emission scenarios, SPEI values continued to decline. In the RCP8.5 scenario, the area of severe drought was large. To sum up, in the future climate scenario, the degree of drought in the Huang-Huai-Hai region will be alleviated to some extent with the increase of rainfall, but with the increase of greenhouse gas concentration, the degree of drought will be further intensified, posing a huge challenge to agricultural water use in the region. This study provides a theoretical foundation for alleviating drought in the Huang-Huai-Hai region in future climate scenarios.

Predicting Climate Change Impact on Water Productivity of Irrigated Rice in Malaysia Using FAO-AquaCrop Model

Water productivity (WP) is a key indicator of agricultural water management, since it affects the quantity of water used for crop yield in various management scenarios. This study evaluated the WP of irrigated rice due to a changing climate in the Northwest Selangor Rice Irrigation Scheme (NSRIS) by using field experimental data and the FAO-AquaCrop Model. Pertinent soil, water, climate, and crop data were acquired by executing a field investigation during the off-season (dry season, January–April) and main season (wet season, July–October) in 2017. The AquaCrop 6.0 model was calibrated and validated using the measured data. A Climate-smart Decision Support System (CSDSS) with an ensemble of 10 Global Climate Models (GCMs) was used to downscale climate variables under RCP4.5, RCP6.0, and RCP8.5 emission scenarios during baseline (1976 to 2005) and future (2020 to 2099) periods. The AquaCrop model fairly predicted rice yields under field conditions with root-mean-square error (RMSE), mean absolute error (MAE), prediction error (PE) and index of agreement (d) between the observed and estimated yields of 0.173, 0.157, −0.31 to 5.4 and 0.78, respectively for the off-season; and 0.167, 0.127, −5.6 to 2.3 and 0.73, respectively for the main season. It predicted a 10% decrease in actual crop evapotranspiration (ETc) in both crop seasons in the future. The WP of rice based on total water input (WPIrr+RF), applied irrigation (WPIrr), and actual crop evapotranspiration (WPETc) will likely increase by 14–24%, 14–19%, and 17–29%, respectively under the three RCP emission scenarios in the off-season. The likely increase in WP for the corresponding base is 13–22%, 15–24%, and 14–25% in the main season. Various agronomic management options linked to WP will most likely become important in making crucial decisions to cope with the risk of impacts on climate change.