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Three Coupled Model Intercomparison Project Phase 5 models involved in the G4 experiment of the Geoengineering Model Inter-comparison Project (GeoMIP) project were used to investigate the impact of stratospheric aerosol injection (SAI) on the mean surface air temperature and precipitation extremes in Africa.

This impact was examined under G4 and Representative Concentration Pathway (RCP) 4.5 scenarios on the total precipitation, the number of rainy days (RR1) and of days with heavy rainfall (R20 mm), the rainfall intensity (SDII), the maximum length of consecutive wet (CWD) and dry (CDD) days and on the maximum rainfall in five consecutive days (Rx5day) across four regions: Western Africa (WAF), Eastern Africa (EAF), Northern Africa and Southern Africa (SAF).

During the 50 years (2020–2069) of SAI, mean continental warming is −0.40°C lower in G4 than under RCP4.5. During the post-injection period (2070–2090), the temperature continues to increase, but at a lower rate (−0.19°C) than in RCP4.5. During SAI, annual rainfall in G4 is significantly greater than in RCP4.5 over the high latitudes (especially over SAF) and lower over the tropics. The termination of SAI leads to a significant increase of rainfall over Sahel and EAF and a decrease over SAF and Guinea Coast (WAF).

Compared to RCP4.5, SAI will contribute to reducing significantly regional warming but with a significant decrease of rainfall in the tropics where rainfed agriculture account for a large part of the economies. After the SAI period, the risk of drought over the extratropical regions (especially in SAF) will be mitigated, while the risk of floods will be exacerbated in the Central Sahel.

To meet the Paris Agreement, African countries will implement mitigation measures to contribute to keep the surface air temperature below 2°C. Geoengineering with SAI is suggested as an option to meet this challenge, but its implication on the African climate system needs a deep investigation in the aim to understand the impacts on temperature and precipitation extremes. To the best of the authors’ knowledge, this study is the first to investigate the potential impact of SAI using the G4 experiment of GeoMIP on temperature and precipitation extremes of the African continent.

The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the Mongolian Plateau when the mean global warming is well below 2°C or limited to 1.5°C.

In total, 30 model simulations of consecutive temperature and precipitation days from Coupled Model Inter-comparison Project Phase 5 (CMIP5) are assessed in comparison with the 111 meteorological monitoring stations from 1961–2005. Multi-model ensemble and model relative error were used to evaluate the performance of CMIP5 models. Slope and the Mann–Kendall test were used to analyze the magnitude of the trends and evaluate the significance of trends of snow depth (SD) from 1981 to 2014 in the Mongolian Plateau.

Some models perform well, even better than the majority (80%) of the models over the Mongolian Plateau, particularly HadGEM2-CC, CMCC-CM, BNU-ESM and GFDL-ESM2M, which simulate best in consecutive dry days (CDD), consecutive wet days (CWD), cold spell duration indicator (CSDI) and warm spell duration indicator (WSDI), respectively. Emphasis zones of WSDI on SD were deeply analysed in the 1.5 and 2 °C global warming period above pre-industrial conditions, because it alone has a significant negative relation with SD among the four indices. It is warmer than before in the Mongolian Plateau, particularly in the southern part of the Mongolian Plateau, indicating less SD.

Providing climate extremes and SD data sets with different spatial-temporal scales over the Mongolian Plateau. Zoning SD potential risk areas and proposing adaptations to promote regional sustainable development.

Climatic extreme events are predicted to occur more frequently and intensely and will significantly threat the living of residents in arid and semi-arid regions. Therefore, this study aims to assess climatic extremes’ response to the emerging climate change mitigation strategy using a marine cloud brightening (MCB) scheme.

Based on Hadley Centre Global Environmental Model version 2-Earth System model simulations of a MCB scheme, this study used six climatic extreme indices [i.e. the hottest days (TXx), the coolest nights (TNn), the warm spell duration (WSDI), the cold spell duration (CSDI), the consecutive dry days (CDD) and wettest consecutive five days (RX5day)] to analyze spatiotemporal evolution of climate extreme events in the arid Sahara-Sahel-Arabian Peninsula Zone with and without MCB implementation.

Compared with a Representative Concentration Pathways 4.5 scenario, from 2030 to 2059, implementation of MCB is predicted to decrease the mean annual TXx and TNn indices by 0.4–1.7 and 0.3–2.1°C, respectively, for most of the Sahara-Sahel-Arabian Peninsula zone. It would also shorten the mean annual WSDI index by 118–183 days and the mean annual CSDI index by only 1–3 days, especially in the southern Sahara-Sahel-Arabian Peninsula zone. In terms of extreme precipitation, MCB could also decrease the mean annual CDD index by 5–25 days in the whole Sahara and Sahel belt and increase the mean annual RX5day index by approximately 10 mm in the east part of the Sahel belt during 2030–2059.

The results provide the first insights into the impacts of MCB on extreme climate in the arid Sahara-Sahel-Arabian Peninsula zone.

The purpose of this paper is to investigate the best frequency description of a chain dependent Markov process for the daily simulation of precipitation. The influence of the order of the Markov chain model to simulate daily precipitation occurrence is evaluated. A mixed‐order model is constructed and compared to a simple first‐order model to evaluate the importance of the model order for the pricing of a rainfall index put option.

For the first time a mixed‐order Markov chain model is presented where the monthly varying order was chosen based on a Bayesian information criteria analysis of rainfall data for one weather station in the US. The outcome of this model is compared to simpler Markov models and to burn analysis results.

The comparison indicate that there is only a slightly better representation of the rain statistics in the theoretically best mixed‐order Markov chain model compared to a more simple first‐order model. Clear differences between the daily simulation and the burn method are found when pricing a put option on a rainfall index. All daily simulation models underestimate the volatility of the monthly rainfall amount especially in the summer months.

To assess the robustness and any geographical dependence of the bias in the volatility a systematic analysis could be applied to more weather stations across the US in further studies.

The bias in the volatility has significant influence on the price of the put option considered here and limits the use of such a model for risk analyses, e.g. for an extreme event cover.

For the first time a multi‐order Markov chain model is applied to price a precipitation derivative. While the focus of previous studies was the appropriate choice for the intensity process, the importance of the frequency process is investigated in this paper.

The purpose of this paper is to assess the extent of climate change likely to be manifested in the MENA region using statistical tools as well as outputs from physics‐based General Circulation Models (GCMs).

Atmospheric temperature and precipitation primarily capture climate change features and are considered the drivers of other manifestations of climate change such as rises in sea‐level, tropical cyclone intensities, severe floods, prolonged droughts, and retreating ice. Data on atmospheric temperature and precipitation have been statistically analysed for trend, distribution and variability in this study. Long‐range prediction is then made using time series analysis. Long‐range projections have also been made by many investigators using physics‐based GCMs and the Fourth Assessment Report of IPCC provides a summary. IPCC projections are not indisputable because of some inherent limitations of GCMs. A comparative study is made between statistical predictions and IPCC projections, as well as forecasts from some GCMs specifically applied to the region, to develop a more reliable forecast scenario. Water resources projects are quite vulnerable to changes in atmospheric temperature and precipitation amounts. The various aspects of planning, design and management of water resources projects which are likely to be influenced by climate change are discussed.

There is considerable variability in atmospheric temperature and precipitation in recent observations but if the variability is filtered out and the underlying trend extrapolated it is found that there is in general an agreement between IPCC projections and statistical predictions. For rise in atmospheric temperature projections made from many GCMs applied to the region, as well as projections summarised in the Fourth Assessment Report of IPCC, appear to be good estimates to be included in design considerations. For precipitation, statistical predictions are perhaps a better choice because GCM projections are less reliable with precipitation since associated meteorological processes occur at a much smaller scale than the grid size of a GCM. For low‐lying coastal regions sea‐level rise and more frequent extreme climatic events such as tropical cyclones add to the dimensionality of design considerations especially for infrastructure design.

This paper presents a comparative study of possible climate change in the long‐term between physics‐based model projections and statistical predictions. This should provide greater insight into climate change that is expected in MENA and reduce uncertainty, thereby instilling greater confidence in water resources planners and practitioners to incorporate climate change aspects into decision making. This research is believed to be particularly helpful because of scant research work done on this part of the globe on climate change.

This study aims to present the climate change effect on soil moisture regimes in Mexico in a global 1.5°C warming scenario.

The soil moisture regimes were determined using the Newhall simulation model with the database of mean monthly precipitation and temperature at a scale of 1: 250,000 for the current scenario and with the climate change scenarios associated with a mean global temperature increase of 1.5°C, considering two Representative Concentration Pathways, 4.5 and 8.5 W/m² and three general models of atmospheric circulation, namely, GFDL, HADGEM and MPI. The different vegetation types of the country were related to the soil moisture regimes for current conditions and for climate change.

According to the HADGEM and MPI models, almost the entire country is predicted to undergo a considerable increase in soil moisture deficit, and part of the areas of each moisture regime will shift to the next drier regime. The GFDL model also predicts this trend but at smaller proportions.

The changes in soil moisture at the regional scale that reveal the impacts of climate change and indicate where these changes will occur are important elements of the knowledge concerning the vulnerability of soils to climate change. New cartography is available in Mexico.

The Monsoon Asian region has a much wider rainfall distribution than other regions of the world. The countries in this region are characterized mostly by floods and typhoons, which result from the interplay among the ocean, the atmosphere, and the land. Thus, many factors affect the strength of the rainfall, including sea surface temperatures in the Indian and Pacific Oceans, variations in solar output, land snow cover and soil moisture over the Asian continent, and the position and strength of prevailing winds. The links between these factors and monsoons appear to wax and wane over time, and the observational record is too short to explain this longer-term variability. Precipitation and surface wind maps of Asia during the summer months of June to August show the average spatial patterns of monsoon circulation and moisture.

The purpose of this paper is to evaluate the seasonal and spatial variations in the statistical descriptors of the Markov chain model as well as the expected values of the length of dry and wet days and to estimate the probability of dry and rainy sequences in the state of Santa Catarina.

Daily rainfall data from 1970 to 2013 of five rainfall stations in the state of Santa Catarina were used. To model the sequence of dry and wet days, the first order of the Markov chain was used. The statistical descriptors of the Markov model were evaluated, as well as the expected values of the length of dry and wet days and the number of dry and rainy days for each month. Along with geometric distribution, the probability of occurrence of sequences of dry and rainy days was determined. The adherence of the data to geometric distribution was evaluated using the Kolmogorov-Smirnov test.

The results showed that there is a seasonal and spatial variation in Markov model descriptors and also in the duration of the dry and rainy periods. These variations may be related to the mechanisms responsible for the formation and distribution of rainfall in the state, such as the air masses and relief. The Lages station, located in the Plateau of Santa Catarina, had the highest P₀₀ values, reflecting more stable conditions of the atmosphere. In autumn and winter, no marked differences were found between the coastal stations and west of the state. The geometric distribution was adequate for estimating the probability of dry and rainy days.

Although some work has already been carried out on the modeling of the Markov chain in the state of Santa Catarina, this study was found to be more complete with the use of various statistical descriptors of the model and its application in estimating the duration of the cycles of dry and wet periods and the number of rainy days in the period.

The authors determine the effectiveness of the Rainfall Index Annual Forage Program (RIAFP) in offsetting yield risk of winter annual forage growers. The authors also evaluate the effectiveness in reducing risk of potential alternative weather indices.

The RIAFP is designed to compensate forage producers when yield losses occur. Prior research found weak correlation between the rainfall index and actual winter annual forage yields. The authors use long-term small-plot variety trials of rye, ryegrass, wheat, triticale and oats with rainfall recorded on site and measure the correlation of the index with actual rainfall and actual yields. The alternative indices include frequency of precipitation events and of days with temperature below freezing.

The correlation between actual rainfall and the current RMA index was strongly positive as in previous research. Correlations between forage yields and monthly intervals of the current RMA index were mostly statistically insignificant, and many had an unexpected sign. All indices had some correlations that were inconsistent across time intervals and forage variety. The inconsistent signs suggest a nonlinear relationship with weather and forage yield, indicating that rainfall can be too much or too little. The number of days below freezing has the most potential of the three measures examined.

Producers should view the winter forage RIAFP as a risk-increasing income-transfer farm program. A product to reduce the risk for forage producers may need to use a crop growth simulation model or another approach that can capture the nonlinearity.

Considerably more data were considered than in past research. Past research did not consider alternative weather indices. The program should be continued if its goal is to serve as disguised income transfer, but it should be discontinued if its goal is to reduce risk.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.