Climate change projection using statistical downscaling model over southern coastal Iran.

Iran is highly vulnerable to climate change, particularly evident in shifting precipitation and temperature patterns, especially in its southern coastal region. With these changing climate conditions, there is an urgent need for practical and adaptive management of water resources and energy supply to address the challenges posed by future climate change. Over the next two to three decades, the effects of climate change, such as precipitation and temperature, are expected to worsen, posing greater risks to water resources, agriculture, and infrastructure stability. Therefore, this study aims to evaluate the alterations in mean daily temperature (Tmean) and total daily rainfall (rrr24) utilizing climate change scenarios from both phases 5 and 6 of the Coupled Model Inter-comparison Project (CMIP5 and CMIP6, respectively) in the southern coastal regions of Iran (Hormozgan province), specifically north of the Strait of Hormuz. The predictions were generated using the Statistical Downscaling Model (SDSM) and National Centre for Environmental Prediction (NCEP) predictors, incorporating climate change scenarios from CMIP5 with Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 and CMIP6 with Shared Socioeconomic Pathways (SSPs) 1, 2, and 5. The analysis was conducted for three distinct time periods: the early 21st century (2021-2045), middle 21st century (2046-2071), and late 21st century (2071-2095). The results indicated that the CMIP5 model outperformed the CMIP6 model in simulating and predicting Tmean and rrr24. In addition, a significant increase in Tmean was observed across all the scenarios and time periods, with the most pronounced trend occurring in the middle and late 21st century future periods. This increase was already evident during the base period of 2021-2045 across all scenarios. Moreover, the fluctuations in precipitation throughout the region and across all scenarios were significant in the three examined future periods. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of Tmean (+1.22 °C) in Bandar Lengeh station in 2071-2095 period. The lowest change magnitude of Tmean among CMIP5 scenarios was found in RCP4.5 (-1.94 °C) in Ch station in 2046-2070 period. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of rrr24 (+150.2 mm) in Chabahar station in 2071-2095 period. The lowest change magnitude of rrr24 among CMIP5 scenarios was found in RCP8.5 (-25.8 mm) in Bandar Abbas station in 2046-2070 period. In conclusion, the study reveals that the coastal area of Hormozgan province will experience rising temperatures and changing rainfall patterns in the future. These changes may lead to challenges such as increased water and energy consumption, heightened risks of droughts or floods, and potential damage to agriculture and infrastructure. These findings offer valuable insights for implementing local mitigation policies and strategies and adapting to emerging climate changes in Hormozgan's coastal areas. For example, utilizing water harvesting technologies, implementing watershed management practices, and adopting new irrigation systems can address challenges like water consumption, agricultural impacts, and infrastructure vulnerability. Future research should accurately assess the effect of these changes in precipitation and temperature on water resources, forest ecosystems, agriculture, and other infrastructures in the study area to implement effective management measures.

Bias-Corrected CMIP5 Projections for Climate Change and Assessments of Impact on Malaria in Senegal under the VECTRI Model.

On the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. Extreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. This study aims to understand the impact of future climate change on malaria transmission using, for the first time in Senegal, the ICTP's community-based vector-borne disease model, TRIeste (VECTRI). This biological model is a dynamic mathematical model for the study of malaria transmission that considers the impact of climate and population variability. A new approach for VECTRI input parameters was also used. A bias correction technique, the cumulative distribution function transform (CDF-t) method, was applied to climate simulations to remove systematic biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) that could alter impact predictions. Beforehand, we use reference data for validation such as CPC global unified gauge-based analysis of daily precipitation (CPC for Climate Prediction Center), ERA5-land reanalysis, Climate Hazards InfraRed Precipitation with Station data (CHIRPS), and African Rainfall Climatology 2.0 (ARC2). The results were analyzed for two CMIP5 scenarios for the different time periods: assessment: 1983-2005; near future: 2006-2028; medium term: 2030-2052; and far future: 2077-2099). The validation results show that the models reproduce the annual cycle well. Except for the IPSL-CM5B model, which gives a peak in August, all the other models (ACCESS1-3, CanESM2, CSIRO, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, and IPSL-CM5B) agree with the validation data on a maximum peak in September with a period of strong transmission in August-October. With spatial variation, the CMIP5 model simulations show more of a difference in the number of malaria cases between the south and the north. Malaria transmission is much higher in the south than in the north. However, the results predicted by the models on the occurrence of malaria by 2100 show differences between the RCP8.5 scenario, considered a high emission scenario, and the RCP4.5 scenario, considered an intermediate mitigation scenario. The CanESM2, CMCC-CM, CMCC-CMS, inmcm4, and IPSL-CM5B models predict decreases with the RCP4.5 scenario. However, ACCESS1-3, CSIRO, NRCM-CM5, GFDL-CM3, GFDL-ESM2G, and GFDL-ESM2M predict increases in malaria under all scenarios (RCP4.5 and RCP8.5). The projected decrease in malaria in the future with these models is much more visible in the RCP8.5 scenario. The results of this study are of paramount importance in the climate-health field. These results will assist in decision-making and will allow for the establishment of preventive surveillance systems for local climate-sensitive diseases, including malaria, in the targeted regions of Senegal.

Water balance components and climate extremes over Brazil under 1.5 °C and 2.0 °C of global warming scenarios.

This work aimed to evaluate changes in water balance components (precipitation, evapotranspiration, and water availability) and precipitation extremes projected under global warming levels (GWLs) of 1.5 °C and 2 °C, in Brazil. An ensemble of eight twenty-first-century projections with the Eta Regional Climate Model and their driving Global Climate Models (CanESM2, HadGEM2-ES, MIROC5, and BESM) were used. Projections of two Representative Concentration Pathway scenarios, RCP4.5 and RCP8.5, considered intermediate and high concentration, respectively, were used. The results indicate that the RCP8.5 scenario under 2 °C GWL is likely to have a higher impact on the water balance components, amplifying trends in drier conditions and increasing the number of consecutive dry days in some regions of Brazil, particularly in the North and Northeast regions. On the other hand, the projections indicate the opposite sign for the South region, with trends toward wetter conditions and significant increases in extreme rainfall. The 0.5 °C difference between the GWLs contributes to intensifying reductions (increases) from 4 to 7% in water availability, mainly in the North-Northeast (South) regions. The projected changes could have serious consequences, such as increases in the number of drought events in hydrographic regions of the Northeast region of Brazil and increases in flood events in the South of the country. The results here presented can contribute to the formulation of adaptive planning strategies aimed at ensuring Brazil's water security towards climate change. Supplementary Information: The online version contains supplementary material available at 10.1007/s10113-023-02042-1.

Projected mid-century rainfall erosivity under climate change over the southeastern United States.

Recent observations and climate change projections indicate that changes in rainfall energy, intensity, duration, and frequency, which determine the erosive power of rainfall, will amplify erosion rates around the world. However, the magnitude and scope of these future changes in erosive power of rainfall remain largely unknown, particularly at finer-resolutions and local scales. Due to a lack of available projected future sub-hourly climate data, previous studies relied on aggregates (hourly, daily) rainfall data. The erosivity for the southeastern United States in this study was calculated using the RUSLE2 erosivity calculation method without data limitation and a recently published 15-min precipitation dataset. This precipitation data was derived from five NA-CORDEX climate models' precipitation products under the Representative Concentration Pathway (RCP) 8.5 scenario. In this dataset, hourly climate projections of precipitation were bias-corrected and temporally downscaled to 15-min resolution for 187 locations with collocated 15-min precipitation observations. Precipitation, erosivity (R-factor), and erosivity density (ED) estimations were provided for historical (1970-1999) and future (2030-2059) time periods. Ensemble results for projected values (as compared to historical values) showed increase in precipitation, erosivity, and erosivity density by 14 %, 47 %, and 29 %, respectively. The future ensemble model showed an average annual R-factor of 11,237±1299 MJ mm ha-1h-1yr-1. These findings suggest that changes in rainfall intensity, rather than precipitation amount, may be driving the change in erosivity. However, the bias correction and downscaling limitations inherent in the original precipitation dataset and this study's analyses obscured this particular result. In general, coastal and mountainous regions are expected to experience the greatest absolute increase in erosivity, while other inland areas are expected to experience the greatest relative change. This study offers a novel examination of projected future precipitation characteristics in terms of erosivity and potential future erosion.

Modelling the impact of climate change on dengue outbreaks and future spatiotemporal shift in Pakistan.

Climate change has a significant impact on the intensity and spread of dengue outbreaks. The objective of this study is to assess the number of dengue transmission suitable days (DTSD) in Pakistan for the baseline (1976-2005) and future (2006-2035, 2041-2070, and 2071-2099) periods under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Moreover, potential spatiotemporal shift and future hotspots of DTSD due to climate change were also identified. The analysis is based on fourteen CMIP5 models that have been downscaled and bias-corrected with quantile delta mapping technique, which addresses data stationarity constraints while preserving future climate signal. The results show a higher DTSD during the monsoon season in the baseline in the study area except for Sindh (SN) and South Punjab (SP). In future periods, there is a temporal shift (extension) towards pre- and post-monsoon. During the baseline period, the top ten hotspot cities with a higher frequency of DTSD are Karachi, Hyderabad, Sialkot, Jhelum, Lahore, Islamabad, Balakot, Peshawar, Kohat, and Faisalabad. However, as a result of climate change, there is an elevation-dependent shift in DTSD to high-altitude cities, e.g. in the 2020s, Kotli, Muzaffarabad, and Drosh; in the 2050s, Garhi Dopatta, Quetta, and Zhob; and in the 2080s, Chitral and Bunji. Karachi, Islamabad, and Balakot will remain highly vulnerable to dengue outbreaks for all the future periods of the twenty-first century. Our findings also indicate that DTSD would spread across Pakistan, particularly in areas where we have never seen dengue infections previously. The good news is that the DTSD in current hotspot cities is projected to decrease in the future due to climate change. There is also a temporal shift in the region during the post- and pre-monsoon season, which provides suitable breeding conditions for dengue mosquitos due to freshwater; therefore, local authorities need to take adaption and mitigation actions.

Australian farm dams are becoming less reliable water sources under climate change.

Worldwide food production is under ever-increasing demand. Meanwhile, climate change is disrupting rainfall and evaporation patterns, making agriculture freshwater supplies more uncertain. IPCC models predict an increased variability in rainfall and temperature over most of the globe under climate change. Yet, the effects of climate variability on water security remain poorly resolved. Here we used satellite images and deep-learning convolutional neural networks to analyse the impacts of annual averages, seasonality, climate anomaly, and temporal autocorrelation (or climate reddening) for rain and temperature on the water levels of >100,000 Australian farm dams across 55 years. We found that the risk of empty farm dams increased with warmer annual temperatures, lower yearly rainfall, stronger seasonality, reduced climate anomalies, and higher temporal autocorrelation. We used this information to develop a predictive model and estimate the likelihood of water limitations in farm dams between 1965 and 2050 using historical data and Coupled Model Intercomparison Project Phase 5 (CMIP5) at two climate change scenarios. Results showed that the frequency of empty water reserves has increased 2.5-fold since 1965 and will continue to increase across most (91%) of Australia. We estimated a 37% decline in rural areas with year-round water supplies between 1965 (457,076 km2) and 2050 (285,998 km2). Our continental-scale assessment documents complex temporal and spatial impacts of climate change on agricultural water security, with ramifications for society, economy, and the environment.

Impacts of 1.5 °C and 2 °C global warming on Eucalyptus plantations in South America.

Even if the maximum global warming thresholds established by the Paris Agreement (1.5 and 2 °C relative to pre-industrial levels) are not exceeded, part of the climate system impacts resulting from this warming will be unavoidable. Forestry industries may be especially vulnerable, due to water shortages and the inability of growing certain forest species. An important part of the South American economy depends on the forestry sector (between 2 to ~7% of the Gross Domestic Product), mainly products derived from Eucalyptus, and so evaluating water availability considering the temperature thresholds established by the Paris Agreement will be fundamental. This study analyzed increased global average temperatures at 1.5 °C and 2 °C, and the impacts on water availability, using the Climatic Water Balance (CWB), and also studied possible impacts on Eucalyptus plantations in South America. Monthly temperature and precipitation data obtained from a set of simulations and projections of 26 General Circulation Models (GCMs) were used, in four Representative Concentration Pathway (RCP) scenarios. The CWB was calculated for three periods: i) the pre-industrial period (1861-1890), ii) the present period (1975-2005), and iii) the period when temperature projections are expected to reach global average increases of 1.5 °C and 2 °C. Due to changes in the CWB, with increases in actual evapotranspiration, water deficits, and a reduced water surplus, Eucalyptus plantations will be negatively affected and economically unfeasible for about 49.2% to 56.7% of all of South America, including a large part of the Amazon region, northern South America, midwestern and northeastern Brazil, western portions of Bolivia, Paraguay, central/northern Argentina, and northern Chile. Only some parts of South America, like the southern and southeastern regions of Brazil, Uruguay, southern Argentina and Chile, Andes Mountain Range, and northwestern South America, will not suffer water deficits, and Eucalyptus plantations will be less impacted in these regions. Large parts of South America will suffer from changes in water availability. The future of the forestry industry, and especially Eucalyptus plantations in these regions, will depend on urgent and effective adaptation measures.

Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models.

Earth system models (ESMs) have been widely used to simulate global terrestrial carbon fluxes, including gross primary production (GPP) and net primary production (NPP). Assessment of such GPP and NPP products can be valuable for understanding the efficacy of certain ESMs in simulating the global carbon cycle and future climate impacts. In this work, we studied the model performance of 22 ESMs participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) by comparing historical GPP and NPP simulations with satellite data from MODIS and further evaluating potential model improvement from CMIP5 to CMIP6. In CMIP6, the average global total GPP and NPP estimated by the 22 ESMs are 16% and 13% higher than MODIS data, respectively. The multi-model ensembles (MME) of the 22 ESMs can fairly reproduce the spatial distribution, zonal distribution and seasonal variations of both GPP and NPP from MODIS. They perform much better in simulating GPP and NPP for grasslands, wetlands, croplands and other biomes than forests. However, there are noticeable differences among individual ESM simulations in terms of overall fluxes, temporal and spatial flux distributions, and fluxes by biome and region. The MME consistently outperforms all individual models in nearly every respect. Even though several ESMs have been improved in CMIP6 relative to CMIP5, there is still much work to be done to improve individual ESM and overall CMIP performance. Future work needs to focus on more comprehensive model mechanisms and parametrizations, higher resolution and more reasonable coupling of land surface schemes and atmospheric/oceanic schemes.

Spatiotemporal dynamics of vegetation in China from 1981 to 2100 from the perspective of hydrothermal factor analysis.

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year-1. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson's Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China's vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model.

Evaluation and Future Projection of Extreme Climate Events in the Yellow River Basin and Yangtze River Basin in China Using Ensembled CMIP5 Models Data.

The Yellow River Basin (YLRB) and Yangtze River Basin (YZRB) are heavily populated, important grain-producing areas in China, and they are sensitive to climate change. In order to study the temporal and spatial distribution of extreme climate events in the two river basins, seven extreme temperature indices and seven extreme precipitation indices were projected for the periods of 2010-2039, 2040-2069, and 2070-2099 using data from 16 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and the delta change and reliability ensemble averaging (REA) methods were applied to obtain more robust ensemble values. First, the present evaluation indicated that the simulations satisfactorily reproduced the spatial distribution of temperature extremes, and the spatial distribution of precipitation extremes was generally suitably captured. Next, the REA values were adopted to conduct projections under different representative concentration pathway (RCP) scenarios (i.e., RCP4.5, and RCP8.5) in the 21st century. Warming extremes were projected to increase while cold events were projected to decrease, particularly on the eastern Tibetan Plateau, the Loess Plateau, and the lower reaches of the YZRB. In addition, the number of wet days (CWD) was projected to decrease in most regions of the two basins, but the highest five-day precipitation (Rx5day) and precipitation intensity (SDII) index values were projected to increase in the YZRB. The number of consecutive dry days (CDD) was projected to decrease in the northern and western regions of the two basins. Specifically, the warming trends in the two basins were correlated with altitude and atmospheric circulation patterns, and the wetting trends were related to the atmospheric water vapor content increases in summer and the strength of external radiative forcing. Notably, the magnitude of the changes in the extreme climate events was projected to increase with increasing warming targets, especially under the RCP8.5 scenario.

A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments.

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modeling community standards and value checking for outlier detection.

Understanding the effect of climate change in the distribution and intensity of malaria transmission over India using a dynamical malaria model.

Efforts have been made to quantify the spatio-temporal malaria transmission intensity over India using the dynamical malaria model, namely, Vector-borne Disease Community Model of International Centre for Theoretical Physics Trieste (VECTRI). The likely effect of climate change in the variability of malaria transmission intensity over different parts of India is also investigated. The Historical data and future projection scenarios of the rainfall and temperature derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) model output are used for this purpose. The Entomological Inoculation Rate (EIR) and Vector are taken as quantifiers of malaria transmission intensity. It is shown that the maximum number of malaria cases over India occur during the Sept-Oct months, whereas the minimum during the Feb-Apr months. The malaria transmission intensity as well as length of transmission season over India is likely to increase in the future climate as a result of global warming.

Projection of climate extremes in China, an incremental exercise from CMIP5 to CMIP6.

This paper presents projections of climate extremes over China under global warming of 1.5, 2, and 3 °C above pre-industrial (1861-1900), based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. Results are compared with what produced by the precedent phase of the project, CMIP5. Model evaluation for the reference period (1985-2005) indicates that CMIP6 models outperform their predecessors in CMIP5, especially in simulating precipitation extremes. Areal averages for changes of most indices are found larger in CMIP6 than in CMIP5. The emblematic annual mean temperature, when averaged over the whole of China in CMIP6, increases by 1.49, 2.21, and 3.53 °C (relative to 1985-2005) for 1.5, 2, and 3 °C above-preindustrial global warming levels, while the counterpart in CMIP5 is 1.20, 1.93 and 3.39 °C respectively. Similarly, total precipitation increases by 5.3%, 8.6%, and 16.3% in CMIP6 and by 4.4%, 7.0% and 12.8% in CMIP5, respectively. The spatial distribution of changes for extreme indices is generally consistent in both CMIP5 and CMIP6, but with significantly higher increases in CMIP6 over Northeast and Northwest China for the hottest day temperature, and South China for the coldest night temperature. In the south bank of the Yangtze River, and most regions around 40°N, CMIP6 shows higher increases for both total precipitation and heavy precipitation. The projected difference between CMIP6 and CMIP5 is mainly attributable to the physical upgrading of climate models and largely independent from their emission scenarios.

Identifying climate change impacts on surface water supply in the southern Central Valley, California.

Mountain regions in arid and semi-arid climates, such as California, are considered particularly sensitive to climate change because global warming is expected to alter snowpack storage and related surface water supply. It is therefore important to accurately capture snowmelt processes in watershed models for climate change impact assessment. In this study we use the Soil and Water Assessment Tool (SWAT) to estimate projected changes in snowpack and streamflow in four alpine tributaries to the agriculturally important but less studied southern Central Valley, California. Watershed responses are evaluated for four CMIP5 climate models (HadGEM_ES, CNRM-CM5, CanESM2 and MIROC5) and two emission scenarios (RCP 4.5 and RCP 8.5) for 2020-2099. SWAT models are calibrated following a dual-objective, lumped calibration approach with an automatic calibration against observed streamflow (stage 1) and a manual calibration against reconstructed Parallel Energy Balance (ParBal) snow water equivalent (SWE) data (stage 2). Results indicate that under a warming climate, peak streamflow is expected to increase 0.5-4 times in magnitude in the coming decades and to arrive 2-4 months earlier in the year because of earlier snowmelt. In the foreseeable future, snow cover will reduce gradually in the lower elevations and diminish at higher rates at higher elevation towards the end of the 21st century. Surface water supply is predicted to increase in the southern Central Valley under the evaluated scenarios but increased temporal variability (wetter wet seasons and drier dry seasons) will create new challenges for managing supply. The study further highlights that the use of remote sensing based, reconstructed SWE data could fill the current gap of limited in-situ SWE observations to improve the snow calibration of SWAT to better predict climate change impacts in semi-arid, snow-dominated watersheds.

Regional Climate Sensitivity of Climate Extremes in CMIP6 Versus CMIP5 Multimodel Ensembles.

We analyze projected changes in climate extremes (extreme temperatures and heavy precipitation) in the multimodel ensembles of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). The results reveal close similarity between both ensembles in the regional climate sensitivity of the projected multimodel mean changes in climate extremes, that is, their projected changes as a function of global warming. This stands in contrast to widely reported divergences in global (transient and equilibrium) climate sensitivity in the two multimodel ensembles. Some exceptions include higher warming in the South America monsoon region, lower warming in Southern Asia and Central Africa, and higher increases in heavy precipitation in Western Africa and the Sahel region in the CMIP6 ensemble. The multimodel spread in regional climate sensitivity is found to be large in both ensembles. In particular, it contributes more to intermodel spread in projected regional climate extremes compared with the intermodel spread in global climate sensitivity in CMIP6. Our results highlight the need to consider regional climate sensitivity as a distinct feature of Earth system models and a key determinant of projected regional impacts, which is largely independent of the models' response in global climate sensitivity.

Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6.

Purpose of Review: The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). Recent Findings: The representation of marine biogeochemistry has progressed within the current generation of Earth system models. However, it remains difficult to identify which model updates are responsible for a given improvement. In addition, the full potential of marine biogeochemistry in terms of Earth system interactions and climate feedback remains poorly examined in the current generation of Earth system models. Summary: Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP).

Modeling impacts of future climate change on reservoir storages and irrigation water demands in a Mediterranean basin.

Water storage requirements in the Mediterranean region vary in time and are strongly affected by the local geography and climate conditions. The objective of this study is to assess the implications of climate change on the water balance of an agricultural reservoir in a Mediterranean-climate basin in Turkey throughout the 21st century. A monthly dynamic water balance model is developed to simulate the historical and future water availability in the reservoir. The model is driven by the fine-resolution dynamically downscaled climate data from four GCMs from the CMIP5 archive, namely CCSM4, GFDL-ESM2M, HadGEM2-ES, and MIROC5, under two different representative concentration pathway scenarios (RCP4.5 and RCP8.5), and the hydrologic data projected under the same scenarios. The reservoir outflows, including the reservoir evaporation and downstream irrigation water demands, are also modeled using the projected climate variables. The net irrigation water requirement of the crops in the irrigation system, seasonal evapotranspiration rates, and reservoir evaporation rates are estimated based on the Penman-Monteith Evapotranspiration method (FAO-56 Method). The study investigates whether the future water supply in the reservoir will be sufficient to meet the future irrigation water demands for the years from 2017 to 2100. The results show that under all eight modeled climate change projections, statistically significant increasing trends for the annual irrigation water demands are expected throughout the 21st century. Moreover, higher evapotranspiration rates are predicted under the ensemble average of the RCP8.5 projections, compared to those of the RCP4.5 projections. Ultimately, seven out of eight projections projected insufficient reservoir water levels during the 21st century, especially during the irrigation seasons when higher water demands are expected. These impacts indicate the importance of sustainable water resources management in the region to provide irrigation water from reservoirs, and to sustain agricultural productivity under projected water limitations due to climate change.

Future river basin health assessment through reliability-resilience-vulnerability: Thresholds of multiple dryness conditions.

Increasing dryness conditions under global warming are posing severe threats to water resources management in China. Projecting river basin responses to dryness conditions is beneficial to effectively managing water resources. However, existing studies have seldom considered the impact of multiple dryness conditions on future river basin health under global warming. Therefore, we combine the 3- and 12-month standard precipitation evapotranspiration index (SPEI) and reliability-resilience-vulnerability framework (RRV) to map future river basin health based on the responses of basins across China to different dryness conditions from 2021 to 2050. The calculation is based on downscaled outputs of 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for three future emission scenarios (i.e., RCP2.6, RCP4.5 and RCP8.5). The results show that water deficits are projected to occur in most areas of China and significantly increase in the basins located in the northern part of China in the next 30 years due to global warming effects. The conditions in parts of the basins located in the northern part of China (especially in the Northwest River basins and Yellow River basin) are projected to be unhealthy and deteriorate significantly in the future, while the basins located in the southern part of China are projected to be moderate. The health status is anticipated to be worse under the RCP8.5 scenario than the RCP2.6 and RCP4.5 scenarios. Integrated results from the three thresholds indicated that normal dryness is applicable to most areas of northeastern, northern and southern China, while abnormal dryness is applicable to the remaining areas. Our findings could help reduce the impact of future dryness conditions on water resources and provide insights into risk planning and management for river basins in China under global warming.

Development of Future Heatwaves for Different Hazard Thresholds.

In 2018 and 2019, heatwaves set all-time temperature records around the world and caused adverse effects on human health, agriculture, natural ecosystems, and infrastructure. Often, severe impacts relate to the joint spatial and temporal extent of the heatwaves, but most research so far focuses either on spatial or temporal attributes of heatwaves. Furthermore, sensitivity of heatwaves characteristics to the choice of the heatwave thresholds in a warming climate are rarely discussed. Here, we analyze the largest spatiotemporal moderate heatwaves-that is, three-dimensional (space-time) clusters of hot days-in simulations of global climate models. We use three different hazard thresholds to define a hot day: fixed thresholds (time-invariant climatological thresholds), seasonally moving thresholds based on changes in the summer means, and fully moving thresholds (hot days defined relative to the future climatology). We find a substantial increase of spatiotemporally contiguous moderate heatwaves with global warming using fixed thresholds, whereas changes for the other two hazard thresholds are much less pronounced. In particular, no or very little changes in the overall magnitude, spatial extent, and duration are detected when heatwaves are defined relative to the future climatology using a temporally fully moving threshold. This suggests a dominant contribution of thermodynamic compared to dynamic effects in global climate model simulations. The similarity between seasonally moving and fully moving thresholds indicates that seasonal mean warming alone can explain large parts of the warming of extremes. The strong sensitivity of simulated future heatwaves to hazard thresholds should be considered in the projections of potential future heat-related impacts.

How will southern hemisphere subtropical anticyclones respond to global warming? Mechanisms and seasonality in CMIP5 and CMIP6 model projections.

The anticyclonic high-pressure systems over the southern-hemisphere, subtropical oceans have a significant influence on regional climate. Previous studies of how these subtropical anticyclones will change under global warming have focused on austral summer while the winter season has remained largely uninvestigated, together with the extent to which the dominant mechanisms proposed to explain the multi-model-mean changes similarly explain the inter-model spread in projections. This study addresses these gaps by focusing on the mechanisms that drive the spread in projected future changes across the Coupled Model Intercomparison Project Phase 5 and 6 archives during both the summer and winter seasons. The southern hemisphere anticyclones intensify in strength at their center and poleward flank during both seasons in the future projections analyzed. The inter-model spread in projected local diabatic heating changes accounts for a considerable amount of the inter-model spread in the response of the South Pacific anticyclone during both seasons. However, model differences in projected zonal-mean tropospheric static stability changes, which in turn influence baroclinic eddy growth, are most influential in determining the often-strong increases in sea level pressure seen along the poleward flank of all the anticyclones during both seasons. Increased zonal-mean tropospheric static stability over the subtropics is consistent with the poleward shift in Hadley cell edge and zonal-mean sea level pressure increases. The results suggest that differences in the extent of tropical-upper-tropospheric and subtropical-lower-tropospheric warming in the southern hemisphere, via their influence on tropospheric static stability, will largely determine the fate of the anticyclones over the coming century.