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.

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.

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.

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.

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.

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.

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.

Predicting the spatiotemporal characteristics of flash droughts with downscaled CMIP5 models in the Jinghe River basin of China

Climate warming greatly affects the frequency and intensity of flash droughts, which can cause huge damage to agriculture. It is important to understand the changing rules of future flash droughts and take precautionary measures in advance. Thus, we focused on the flash drought characteristic of the Jinghe River basin using variable infiltration capacity (VIC) model and four-model ensemble in the two representative concentration pathway scenarios. Four-model ensemble mean can well capture hydrological changes in the reference period. The heat wave flash drought (HWFD) and the precipitation deficit flash drought (PDFD) mainly occur in the northern during reference period. The HWFD and PDFD have shown a linear growth trend in the future and both shown higher growth rates in the RCP8.5 scenario. The frequency of occurrence (FOC) increments of flash droughts were relatively high in the southern Jinghe River basin. And the HWFD and the PDFD mainly occurred in May-September. Further results indicate that the contribution of the maximum temperature to HWFD was the biggest (greater than 0.7), followed by evapotranspiration (ET) and soil moisture (SM). The contribution of maximum temperature to PDFD was the biggest (greater than 0.5), followed by precipitation and ET. Global warming in the twenty-first century is likely to lead to intensification of flash droughts. Therefore, measures and suggestions were proposed to effectively respond to flash droughts in our study.

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.

Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 over the Qinghai-Tibet Plateau

A bias-corrected dataset containing daily meteorological data over the Qinghai-Tibet Plateau has been generated using a trend-preserving bias correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach, with a high-quality gridded meteorological dataset based on ground observations (CN05.1). The dataset contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and a downscaled high-resolution dataset (NEX-GDDP), based on CMIP5 models, over the Qinghai-Tibet Plateau (QTP) during 1986–2095. This dataset provides an important reference for the study of future climate change and its impacts in the Qinghai-Tibet Plateau region.

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.

Adverse weather conditions for UK wheat production under climate change

Winter wheat is an important crop in the UK, suited to the typical weather conditions in the current climate. In a changing climate the increased frequency and severity of adverse weather events, which are often localised, are considered a major threat to wheat production. In the present study we assessed a range of adverse weather conditions, which can significantly affect yield, under current and future climates based on adverse weather indices. We analysed changes in the frequency, magnitude and spatial patterns of 10 adverse weather indices, at 25 sites across the UK, using climate scenarios from the CMIP5 ensemble of global climate models (GCMs) and two greenhouse gas emissions (RCP4.5 and RCP8.5). The future UK climate is expected to remain favourable for wheat production, with most adverse weather indicators reducing in magnitude by the mid-21st century. Hotter and drier summers would improve sowing and harvesting conditions and reduce the risk of lodging. The probability of late frosts and heat stress during reproductive and grain filling periods would likely remain small in 2050. Wetter winter and spring could cause issues with waterlogging. The severity of drought stress during reproduction would generally be lower in 2050, however localised differences suggest it is important to examine drought at a small spatial scale. Prolonged water stress does not increase considerably in the UK, as may be expected in other parts of Europe. Climate projections based on the CMIP5 ensemble reveal considerable uncertainty in the magnitude of adverse weather conditions including waterlogging, drought and water stress. The variation in adverse weather conditions due to GCMs was generally greater than between emissions scenarios. Accordingly, CMIP5 ensembles should be used in the assessment of adverse weather conditions for crop production to indicate the full range of possible impacts, which a limited number of GCMs may not provide.

Time-evolving sea-surface warming patterns modulate the climate change response of subtropical precipitation over land

One of the most impactful aspects of climate change is the potential change in water availability. Large populations live in Mediterranean-like regions—so-called because they receive most of their precipitation in winter and experience dry, hot summers—which are highly vulnerable to water stress. It is generally assumed that changes in water availability are proportional to global warming. In this study, we show that this is not the case for Mediterranean-like regions, because of the strong influence of changing patterns of atmospheric circulation induced by patterns of sea-surface-temperature changes. In the Mediterranean itself and in Chile, the projected drying is substantially accelerated relative to global warming, whereas in California, the projected moistening is substantially delayed.

Greenhouse gas (GHG) emissions affect precipitation worldwide. The response is commonly described by two timescales linked to different processes: a rapid adjustment to radiative forcing, followed by a slower response to surface warming. However, additional timescales exist in the surface-warming response, tied to the time evolution of the sea-surface-temperature (SST) response. Here, we show that in climate model projections, the rapid adjustment and surface mean warming are insufficient to explain the time evolution of the hydro-climate response in three key Mediterranean-like areas—namely, California, Chile, and the Mediterranean. The time evolution of those responses critically depends on distinct shifts in the regional atmospheric circulation associated with the existence of distinct fast and slow SST warming patterns. As a result, Mediterranean and Chilean drying are in quasiequilibrium with GHG concentrations, meaning that the drying will not continue after GHG concentrations are stabilized, whereas California wetting will largely emerge only after GHG concentrations are stabilized. The rapid adjustment contributes to a reduction in precipitation, but has a limited impact on the balance between precipitation and evaporation. In these Mediterranean-like regions, future hydro-climate–related impacts will be substantially modulated by the time evolution of the pattern of SST warming that is realized in the real world.

NW Iberian Peninsula coastal upwelling future weakening: Competition between wind intensification and surface heating

Climate change will modify the oceanographic future properties of the NW Iberian Peninsula due to the projected variations in the meteorological forcing, that will intensify local winds and promote surface heating. The Delft3D-Flow model forced with atmospheric conditions provided within the framework of the CORDEX project under the RCP 8.5 greenhouse emission scenario was used to analyse changes in upwelling. Numerical experiments were conducted under high-extreme upwelling conditions for the historical (1976-2005) and future (2070-2099) period. This study also innovates through the exploitation of a numerical modelling approach that includes both shelf and estuarine processes along the coastal zone. Coastal upwelling will be less effective in the future despite the enhancement of upwelling favorable wind patterns previously predicted for this region. Upwelling weakening is due to the future sea surface warming that will increase the stratification of the upper layers hindering the upward displacement of the underlying water, reducing the surface input of nutrients.

Evaluation and bias correction of global climate models in the CMIP5 over the Indian Ocean region

Global climate model (GCM) simulations driven by various emission scenarios are widely used for the projections of future climate change. In this study, an assessment was carried out by using 35 GCMs under Coupled Model Intercomparison Project (CMIP5) in reproducing the present day wind speed changes over six selected regions in the Indian Ocean region based on altimetry-measured merged wind speed product in the Indian Ocean. The relative ranking of the GCMs is performed based on the evaluation of the CMIP5 historical simulations for the period 1993-2005. The skill level of GCMs in representing the various metrics such as annual mean, mean seasonal cycle, linear trend, correlation coefficient, and seasonal standard deviations was accounted for the relative ranking of the GCMs. The models CMCC-CESM, HadGEM2-ES, and GFDL-ESM2G are found to be better for the Arabian Sea region. The GCM products such as HadCM3, CSIRO-Mk3.6.0, HadGEM2-CC, HadGEM2-AO, and MIROC5 were noticed better for the Bay of Bengal (BoB) region. Large bias in wind speed (~ 3 m/s) is observed for the head BoB and the Southern Ocean region. Bias corrections for the present-day Representative Concentration Pathway (RCP) simulations (2006-2016) were performed based on quantile mapping (QM) method, and the present-day wind changes are also compared with observations. The findings from study recommend that suitable bias correction for different GCMs is an essential pre-requisite for climate change studies.

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).

A new statistical downscaling approach for global evaluation of the CMIP5 precipitation outputs: Model development and application

Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been widely used in studies of climate changes related to scenarios at global and regional scales. However, CMIP5 outputs cannot be used directly in analysis of climate changes due to coarse spatial resolution. Here, we proposed a new statistical downscaling method for the downscaling practice of the CMIP5 outputs, i.e. Bias-corrected and station-based Non-linear Regression Downscaling method based on Randomly-Moving Points (BNRD). And up to now, there are only two global downscaled CMIP5 precipitation datasets, i.e. NASA daily downscaled CMIP5 precipitation product and BCSD-based (Bias Correction Spatial Disaggregation) monthly downscaled CMIP5 precipitation product available online, which are both based on BCSD downscaling method. Hence, we evaluated downscaling performance of BNRD by comparing it with the downscaled CMIP5 outputs using the BCSD method in this current study. The results indicate that: (1) during the period for development of the model (1964-2005), the error between downscaled CMIP5 precipitation and GPCC ranges between -50 mm-50 mm at monthly scale. When compared to BCSD-downscaled CMIP5 precipitation, BNRD-downscaled CMIP5 precipitation well reduces errors and avoids underestimation and overestimation of GPCC by BCSD-downscaled CMIP5 precipitation; (2) during period for verification of the downscaling models (2006-2013), the maximum (182 mm), minimum (15 mm) and average (68 mm) RMSEs between BNRD-downscaled CMIP5 precipitation and GPCC are all lower than those between BCSD-downscaled CMIP5 precipitation and GPCC at continental scales. Besides, from the average precipitation viewpoint, BNRD-downscaled CMIP5 precipitation is in higher correlation (around 0.75) with GPCC than BCSD-downscaled CMIP5 precipitation under RCP4.5 and RCP8.5 scenarios at continental scales; (3) BNRD resolved the negative relation to GPCC in the areas near equator, including north part of the South America, southern Africa, northern Australia. In all, BNRD downscaling method developed in this study performs better in describing GPCC changes in both space and time when compared to BCSD and can be used for downscaling practice of CMIP5 and even potentially CMIP6 precipitation outputs over the globe.

CMIP5 wind speed comparison between satellite altimeter and reanalysis products for the Bay of Bengal

A proper evaluation and performance assessment of climate model projections have received considerable attention during the recent past amongst the scientific community. Quality of wind datasets used for analysis is of paramount importance to meteorologists, oceanographers, and climatologist as an essential pre-requisite for modelling needs. This study examined the measured wind speeds obtained from satellite altimetry available from IFREMER/CERSAT, along with two atmospheric reanalysis products ECMWF ERA-Interim and NCEP-CFSR. The reanalysis products and altimeter data were compared with wind speed simulated from 33 different models under WCRP-CMIP5 project for the Bay of Bengal (BoB) region. Study investigated both historical and projections of CMIP5 data providing an opportunity to inter-compare the wind speeds resulting from various emission scenarios with Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0, and 8.5, respectively. The objective is to establish and find out a suitable emission scenario applicable to the BoB region. Temporal and spatial analyses of CMIP5 data infer variability in terms of correlation, bias, and root mean square error. For the historical runs (1991-2005) based on analysis of 29 CMIP5 models, it could be ascertained that the correlation coefficient in wind speed varied between 0.6 and 0.9 and with a bias ranging from - 1.6 to 4 ms^-1. Similar analysis of the CMIP5 projections was carried out with 11 models for RCP 2.6, 29 models for RCP 4.5, 10 models for RCP 6.0, and 28 models for RCP 8.5. Basin-scale mean using altimeter and re-analysis products indicates that RCPs 2.6 and 6.0 showed less correlation with a higher bias for the study region. Analysis of historical model runs signifies that HadGEM2-ES, HadGEM2-AO, HadGEM2-CC, MIROC5, GISS-E2R, and CNRM-CM5 are the best performing models for the study domain. Findings from the study indicate that RCP 4.5 wind speed stands better for the Bay of Bengal region. In a broader perspective, due to various uncertainties involved in climate model outputs, it is imperative to perform a comprehensive analysis amongst multiple data sources to establish and identify the best quality data for scientific needs.

Global warming has increased global economic inequality

We find that global warming has very likely exacerbated global economic inequality, including ∼25% increase in population-weighted between-country inequality over the past half century. This increase results from the impact of warming on annual economic growth, which over the course of decades has accumulated robust and substantial declines in economic output in hotter, poorer countries—and increases in many cooler, wealthier countries—relative to a world without anthropogenic warming. Thus, the global warming caused by fossil fuel use has likely exacerbated the economic inequality associated with historical disparities in energy consumption. Our results suggest that low-carbon energy sources have the potential to provide a substantial secondary development benefit, in addition to the primary benefits of increased energy access.

Understanding the causes of economic inequality is critical for achieving equitable economic development. To investigate whether global warming has affected the recent evolution of inequality, we combine counterfactual historical temperature trajectories from a suite of global climate models with extensively replicated empirical evidence of the relationship between historical temperature fluctuations and economic growth. Together, these allow us to generate probabilistic country-level estimates of the influence of anthropogenic climate forcing on historical economic output. We find very high likelihood that anthropogenic climate forcing has increased economic inequality between countries. For example, per capita gross domestic product (GDP) has been reduced 17–31% at the poorest four deciles of the population-weighted country-level per capita GDP distribution, yielding a ratio between the top and bottom deciles that is 25% larger than in a world without global warming. As a result, although between-country inequality has decreased over the past half century, there is ∼90% likelihood that global warming has slowed that decrease. The primary driver is the parabolic relationship between temperature and economic growth, with warming increasing growth in cool countries and decreasing growth in warm countries. Although there is uncertainty in whether historical warming has benefited some temperate, rich countries, for most poor countries there is >90% likelihood that per capita GDP is lower today than if global warming had not occurred. Thus, our results show that, in addition to not sharing equally in the direct benefits of fossil fuel use, many poor countries have been significantly harmed by the warming arising from wealthy countries’ energy consumption.

A downscaling-disaggregation approach for developing IDF curves in arid regions

Over the past decades, urbanization in Arabian Gulf region expands in flood-prone areas at an unprecedented rate. Chronic water stress and potential changes in extreme rainfall attributed to climate change therefore pose unique challenges in planning and designing water management infrastructures. The objective of this study is to develop a framework to integrate climate change variations into intensity-duration-frequency (IDF) curves in Oman. A two-stage downscaling-disaggregation method was applied with rainfall at Tawi-Atair station in Dhofar region. Potential variations of extreme rainfall in future were examined by eight scenarios composed with two general circulation models (GCMs), two representative concentration pathways (RCPs), and two future periods (2040-2059 and 2080-2099). A stochastic weather generator model was used to downscale rainfall output from GCM grid scale to local scale. Downscaled daily data were then disaggregated to hourly and 5-min series by using K-nearest neighbor (K-NN) technique. Annual maximum rainfall extracted from eight future scenarios and also from present climate (baseline period) was used to develop rainfall intensity-frequency relationships for eight durations range from 5 min to 24 h. Results of the K-NN analysis indicate that the optimum window size of 57 days and 181 h is suitable for hourly and 5-min disaggregation models, respectively. Results also predict that the effects of climate change on the rainfall intensity will be more significant on storms with shorter durations and higher return periods. Moving towards the end of the twenty-first century, the return period of extreme rainfall events is likely to decrease due to intensified rainfall events.

Emergence of robust precipitation changes across crop production areas in the 21st century

A warming climate will affect regional precipitation and hence food supply. However, only a few regions around the world are currently undergoing precipitation changes that can be attributed to climate change. Knowing when such changes are projected to emerge outside natural variability-the time of emergence (TOE)-is critical for taking effective adaptation measures. Using ensemble climate projections, we determine the TOE of regional precipitation changes globally and in particular for the growing areas of four major crops. We find relatively early (<2040) emergence of precipitation trends for all four crops. Reduced (increased) precipitation trends encompass 1-14% (3-31%) of global production of maize, wheat, rice, and soybean. Comparing results for RCP8.5 and RCP2.6 clearly shows that emissions compatible with the Paris Agreement result in far less cropped land experiencing novel climates. However, the existence of a TOE, even under the lowest emission scenario, and a small probability for early emergence emphasize the urgent need for adaptation measures. We also show how both the urgency of adaptation and the extent of mitigation vary geographically.

Reframing Future Risks of Extreme Heat in the United States

The goal of this study is to reframe the analysis and discussion of extreme heat projections to improve communication of future extreme heat risks in the United States. We combine existing data from 31 of the Coupled Model Intercomparison Project Phase 5 models to examine future exposure to extreme heat for global average temperatures of 1.5, 2, 3, and 4 °C above a preindustrial baseline. We find that throughout the United States, historically rare extreme heat events become increasingly common in the future as global temperatures rise and that the depiction of exposure depends in large part on whether extreme heat is defined by absolute or relative metrics. For example, for a 4 °C global temperature rise, parts of the country may never see summertime temperatures in excess of 100 °F, but virtually all of the country is projected to experience more than 4 weeks per summer with temperatures exceeding their historical summertime maximum. All of the extreme temperature metrics we explored become more severe with increasing global average temperatures. However, a moderate climate scenario delays the impacts projected for a 3 °C world by almost a generation relative to the higher scenario and prevents the most extreme impacts projected for a 4 °C world.

Importance of disturbance history on net primary productivity in the world's most productive forests and implications for the global carbon cycle

Analysis of growth and biomass turnover in natural forests of Eucalyptus regnans, the world's tallest angiosperm, reveals it is also the world's most productive forest type, with fire disturbance an important mediator of net primary productivity (NPP). A comprehensive empirical database was used to calculate the averaged temporal pattern of NPP from regeneration to 250 years age. NPP peaks at 23.1 ± 3.8 (95% interquantile range) Mg C ha^-1  year^-1 at age 14 years, and declines gradually to about 9.2 ± 0.8 Mg C ha^-1  year^-1 at 130 years, with an average NPP over 250 years of 11.4 ± 1.1 Mg C ha^-1  year^-1 , a value similar to the most productive temperate and tropical forests around the world. We then applied the age-class distribution of E. regnans resulting from relatively recent historical fires to estimate current NPP for the forest estate. Values of NPP were 40% higher (13 Mg C ha^-1  year^-1 ) than if forests were assumed to be at maturity (9.2 Mg C ha^-1  year^-1 ). The empirically derived NPP time series for the E. regnans estate was then compared against predictions from 21 global circulation models, showing that none of them had the capacity to simulate a post-disturbance peak in NPP, as found in E. regnans. The potential importance of disturbance impacts on NPP was further tested by applying a similar approach to the temperate forests of conterminous United States and of China. Allowing for the effects of disturbance, NPP summed across both regions was on average 11% (or 194 Tg C/year) greater than if all forests were assumed to be in a mature state. The results illustrate the importance of accounting for past disturbance history and growth stage when estimating forest primary productivity, with implications for carbon balance modelling at local to global scales.

Climate change impact assessment on the hydrological regime of the Kaligandaki Basin, Nepal

The Hindu Kush-Himalayan region is an important global freshwater resource. The hydrological regime of the region is vulnerable to climatic variations, especially precipitation and temperature. In our study, we modelled the impact of climate change on the water balance and hydrological regime of the snow dominated Kaligandaki Basin. The Soil and Water Assessment Tool (SWAT) was used for a future projection of changes in the hydrological regime of the Kaligandaki basin based on Representative Concentration Pathways Scenarios (RCP 4.5 and RCP 8.5) of ensemble downscaled Coupled Model Intercomparison Project's (CMIP5) General Circulation Model (GCM) outputs. It is predicted to be a rise in the average annual temperature of over 4°C, and an increase in the average annual precipitation of over 26% by the end of the 21st century under RCP 8.5 scenario. Modeling results show these will lead to significant changes in the basin's water balance and hydrological regime. In particular, a 50% increase in discharge is expected at the outlet of the basin. Snowmelt contribution will largely be affected by climate change, and it is projected to increase by 90% by 2090.Water availability in the basin is not likely to decrease during the 21st century. The study demonstrates that the important water balance components of snowmelt, evapotranspiration, and water yield at higher elevations in the upper and middle sub-basins of the Kaligandaki Basin will be most affected by the increasing temperatures and precipitation.

Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems

Climate change is lengthening the growing season of the Northern Hemisphere extratropical terrestrial ecosystems, but little is known regarding the timing and dynamics of the peak season of plant activity. Here, we use 34-year satellite normalized difference vegetation index (NDVI) observations and atmospheric CO₂ concentration and δ¹³ C isotope measurements at Point Barrow (Alaska, USA, 71°N) to study the dynamics of the peak of season (POS) of plant activity. Averaged across extratropical (>23°N) non-evergreen-dominated pixels, NDVI data show that the POS has advanced by 1.2 ± 0.6 days per decade in response to the spring-ward shifts of the start (1.0 ± 0.8 days per decade) and end (1.5 ± 1.0 days per decade) of peak activity, and the earlier onset of the start of growing season (1.4 ± 0.8 days per decade), while POS maximum NDVI value increased by 7.8 ± 1.8% for 1982-2015. Similarly, the peak day of carbon uptake, based on calculations from atmospheric CO₂ concentration and δ¹³ C data, is advancing by 2.5 ± 2.6 and 4.3 ± 2.9 days per decade, respectively. POS maximum NDVI value shows strong negative relationships (p < .01) with the earlier onset of the start of growing season and POS days. Given that the maximum solar irradiance and day length occur before the average POS day, the earlier occurrence of peak plant activity results in increased plant productivity. Both the advancing POS day and increasing POS vegetation greenness are consistent with the shifting peak productivity towards spring and the increasing annual maximum values of gross and net ecosystem productivity simulated by coupled Earth system models. Our results further indicate that the decline in autumn NDVI is contributing the most to the overall browning of the northern high latitudes (>50°N) since 2011. The spring-ward shift of peak season plant activity is expected to disrupt the synchrony of biotic interaction and exert strong biophysical feedbacks on climate by modifying the surface albedo and energy budget.

Multimodel Evidence for an Atmospheric Circulation Response to Arctic Sea Ice Loss in the CMIP5 Future Projections

Previous single-model experiments have found that Arctic sea ice loss can influence the atmospheric circulation. To evaluate this process in a multimodel ensemble, a novel methodology is here presented and applied to infer the influence of Arctic sea ice loss in the CMIP5 future projections. Sea ice influence is estimated by comparing the circulation response in the RCP8.5 scenario against the circulation response to sea surface warming and CO₂ increase inferred from the AMIPFuture and AMIP4xCO2 experiments, where sea ice is unperturbed. Multimodel evidence of the impact of sea ice loss on midlatitude atmospheric circulation is identified in late winter (January-March), when the sea ice-related surface heat flux perturbation is largest. Sea ice loss acts to suppress the projected poleward shift of the North Atlantic jet, to increase surface pressure in northern Siberia, and to lower it in North America. These features are consistent with previous single-model studies, and the present results indicate that they are robust to model formulation.

Future changes in coverage of 1.5°C and 2°C warming thresholds

The areas covered by 1.5 °C and 2.0 °C warming thresholds under RCP2.6, RCP4.5, and RCP8.5 were analyzed based on 22 CMIP5 models. More than 90% of the model runs are in agreement that by the end of the 21st century, near-surface air temperature changes over ∼5% (∼2%), ∼40% (∼18%), and ∼92% (∼86%) of the globe will cross the 1.5 °C (2.0 °C) threshold under RCP2.6, RCP4.5, and RCP8.5, respectively. Under RCP8.5, nearly the whole of North America, Europe + Russia, Africa, and Asia-Russia will cross the 1.5 °C (2.0 °C) threshold in ∼2050 (∼2060), while the coverage rates over South America and Oceania are ∼80% (∼75%) and ∼50% (∼30%), respectively. The threshold-onset time (TOT) for 2 °C warming is earliest over Europe + Russia and North America, followed by Africa, Asia-Russia, South America, and finally Oceania under the RCP4.5 and RCP8.5 scenarios. The TOT for 1.5 °C is ∼10-30 years ahead of that for 2.0 °C.

Moisture-induced greening of the South Asia over the past three decades

South Asia experienced a weakening of summer monsoon circulation in the past several decades, resulting in rainfall decline in wet regions. In comparison with other tropical ecosystems, quantitative assessments of the extent and triggers of vegetation change are lacking in assessing climate-change impacts over South Asia dominated by crops. Here, we use satellite-based Normalized Difference Vegetation Index (NDVI) to quantify spatial-temporal changes in vegetation greenness, and find a widespread annual greening trend that stands in contrast to the weakening of summer monsoon circulation particularly over the last decade. We further show that moisture supply is the primary factor limiting vegetation activity during dry season or in dry region, and cloud cover or temperature would become increasingly important in wet region. Enhanced moisture conditions over dry region, coinciding with the decline in monsoon, are mainly responsible for the widespread greening trend. This result thereby cautions the use of a unified monsoon index to predict South Asia's vegetation dynamics. Current climate-carbon models in general correctly reproduce the dominant control of moisture in the temporal characteristics of vegetation productivity. But the model ensemble cannot exactly reproduce the spatial pattern of satellite-based vegetation change mainly because of biases in climate simulations. The moisture-induced greening over South Asia, which is likely to persist into the wetter future, has significant implications for regional carbon cycling and maintaining food security.

Projections of climate-driven changes in tuna vertical habitat based on species-specific differences in blood oxygen affinity

Oxygen concentrations are hypothesized to decrease in many areas of the ocean as a result of anthropogenically driven climate change, resulting in habitat compression for pelagic animals. The oxygen partial pressure, pO₂ , at which blood is 50% saturated (P₅₀ ) is a measure of blood oxygen affinity and a gauge of the tolerance of animals for low ambient oxygen. Tuna species display a wide range of blood oxygen affinities (i.e., P₅₀ values) and therefore may be differentially impacted by habitat compression as they make extensive vertical movements to forage on subdaily time scales. To project the effects of end-of-the-century climate change on tuna habitat, we calculate tuna P₅₀ depths (i.e., the vertical position in the water column at which ambient pO₂ is equal to species-specific blood P₅₀ values) from 21st century Earth System Model (ESM) projections included in the fifth phase of the Climate Model Intercomparison Project (CMIP5). Overall, we project P₅₀ depths to shoal, indicating likely habitat compression for tuna species due to climate change. Tunas that will be most impacted by shoaling are Pacific and southern bluefin tunas-habitat compression is projected for the entire geographic range of Pacific bluefin tuna and for the spawning region of southern bluefin tuna. Vertical shifts in P₅₀ depths will potentially influence resource partitioning among Pacific bluefin, bigeye, yellowfin, and skipjack tunas in the northern subtropical and eastern tropical Pacific Ocean, the Arabian Sea, and the Bay of Bengal. By establishing linkages between tuna physiology and environmental conditions, we provide a mechanistic basis to project the effects of anthropogenic climate change on tuna habitats.

Dependence of drivers affects risks associated with compound events

Compound climate extremes are receiving increasing attention because of their disproportionate impacts on humans and ecosystems. However, risks assessments generally focus on univariate statistics. We analyze the co-occurrence of hot and dry summers and show that these are correlated, inducing a much higher frequency of concurrent hot and dry summers than what would be assumed from the independent combination of the univariate statistics. Our results demonstrate how the dependence structure between variables affects the occurrence frequency of multivariate extremes. Assessments based on univariate statistics can thus strongly underestimate risks associated with given extremes, if impacts depend on multiple (dependent) variables. We conclude that a multivariate perspective is necessary to appropriately assess changes in climate extremes and their impacts and to design adaptation strategies.

The impacts of climate change on the abundance and distribution of the Spotted Wing Drosophila (Drosophila suzukii) in the United States and Canada

D. suzukii is a relatively recent and destructive pest species to the North American soft-skinned fruit industry. Understanding this species' potential to shift in abundance and range due to changing climate is an important part of an effective mitigation and management strategy. We parameterized a temperature-driven D. suzukii population dynamics model using temperature data derived from several Global Circulation Models (CMIP5) with a range of relative concentration pathway (RCP) predictions. Mean consensus between the models suggest that without adaptation to both higher prolonged temperatures and higher short-term temperature events D. suzukii population levels are likely to drop in currently higher-risk regions. The potential drop in population is evident both as time progresses and as the severity of the RCP scenario increases. Some regions, particularly in northern latitudes, may experience increased populations due to milder winter and more developmentally-ideal summer conditions, but many of these regions are not currently known for soft-skinned fruit production and so the effects of this population increase may not have a significant impact.

Communicating the deadly consequences of global warming for human heat stress

In December of 2015, the international community pledged to limit global warming to below 2 °C above preindustrial (PI) to prevent dangerous climate change. However, to what extent, and for whom, is danger avoided if this ambitious target is realized? We address these questions by scrutinizing heat stress, because the frequency of extremely hot weather is expected to continue to rise in the approach to the 2 °C limit. We use analogs and the extreme South Asian heat of 2015 as a focusing event to help interpret the increasing frequency of deadly heat under specified amounts of global warming. Using a large ensemble of climate models, our results confirm that global mean air temperature is nonlinearly related to heat stress, meaning that the same future warming as realized to date could trigger larger increases in societal impacts than historically experienced. This nonlinearity is higher for heat stress metrics that integrate the effect of rising humidity. We show that, even in a climate held to 2 °C above PI, Karachi (Pakistan) and Kolkata (India) could expect conditions equivalent to their deadly 2015 heatwaves every year. With only 1.5 °C of global warming, twice as many megacities (such as Lagos, Nigeria, and Shanghai, China) could become heat stressed, exposing more than 350 million more people to deadly heat by 2050 under a midrange population growth scenario. The results underscore that, even if the Paris targets are realized, there could still be a significant adaptation imperative for vulnerable urban populations.

Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States

A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere-ocean general circulation models (AOGCMs; CCSM4, HadGEM2-CC, MIROC5, and MRI-CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways (RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model PnET-BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO₂ effects on vegetation (growth enhancement and increases in water use efficiency (WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce-fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO₂ effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change.

Predicting the responses of forest distribution and aboveground biomass to climate change under RCP scenarios in southern China

In the past three decades, our global climate has been experiencing unprecedented warming. This warming has and will continue to significantly influence the structure and function of forest ecosystems. While studies have been conducted to explore the possible responses of forest landscapes to future climate change, the representative concentration pathways (RCPs) scenarios under the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5) have not been widely used in quantitative modeling research of forest landscapes. We used LANDIS-II, a forest dynamic landscape model, coupled with a forest ecosystem process model (PnET-II), to simulate spatial interactions and ecological succession processes under RCP scenarios, RCP2.6, RCP4.5 and RCP8.5, respectively. We also modeled a control scenario of extrapolating current climate conditions to examine changes in distribution and aboveground biomass (AGB) among five different forest types for the period of 2010-2100 in Taihe County in southern China, where subtropical coniferous plantations dominate. The results of the simulation show that climate change will significantly influence forest distribution and AGB. (i) Evergreen broad-leaved forests will expand into Chinese fir and Chinese weeping cypress forests. The area percentages of evergreen broad-leaved forests under RCP2.6, RCP4.5, RCP8.5 and the control scenarios account for 18.25%, 18.71%, 18.85% and 17.46% of total forest area, respectively. (ii) The total AGB under RCP4.5 will reach its highest level by the year 2100. Compared with the control scenarios, the total AGB under RCP2.6, RCP4.5 and RCP8.5 increases by 24.1%, 64.2% and 29.8%, respectively. (iii) The forest total AGB increases rapidly at first and then decreases slowly on the temporal dimension. (iv) Even though the fluctuation patterns of total AGB will remain consistent under various future climatic scenarios, there will be certain responsive differences among various forest types.

Global warming without global mean precipitation increase?

Global climate models simulate a robust increase of global mean precipitation of about 1.5 to 2% per kelvin surface warming in response to greenhouse gas (GHG) forcing. Here, it is shown that the sensitivity to aerosol cooling is robust as well, albeit roughly twice as large. This larger sensitivity is consistent with energy budget arguments. At the same time, it is still considerably lower than the 6.5 to 7% K(-1) decrease of the water vapor concentration with cooling from anthropogenic aerosol because the water vapor radiative feedback lowers the hydrological sensitivity to anthropogenic forcings. When GHG and aerosol forcings are combined, the climate models with a realistic 20th century warming indicate that the global mean precipitation increase due to GHG warming has, until recently, been completely masked by aerosol drying. This explains the apparent lack of sensitivity of the global mean precipitation to the net global warming recently found in observations. As the importance of GHG warming increases in the future, a clear signal will emerge.

Potential escalation of heat-related working costs with climate and socioeconomic changes in China

Global climate change will increase the frequency of hot temperatures, impairing health and productivity for millions of working people and raising labor costs. In mainland China, high-temperature subsidies (HTSs) are allocated to employees for each working day in extremely hot environments, but the potential heat-related increase in labor cost has not been evaluated so far. Here, we estimate the potential HTS cost in current and future climates under different scenarios of socioeconomic development and radiative forcing (Representative Concentration Pathway), taking uncertainties from the climate model structure and bias correction into account. On average, the total HTS in China is estimated at 38.6 billion yuan/y (US $6.22 billion/y) over the 1979-2005 period, which is equivalent to 0.2% of the gross domestic product (GDP). Assuming that the HTS standards (per employee per hot day) remain unchanged throughout the 21st century, the total HTS may reach 250 billion yuan/y in the 2030s and 1,000 billion yuan/y in 2100. We further show that, without specific adaptation, the increased HTS cost is mainly determined by population growth until the 2030s and climate change after the mid-21st century because of increasingly frequent hot weather. Accounting for the likely possibility that HTS standards follow the wages, the share of GDP devoted to HTS could become as high as 3% at the end of 21st century.

Perspectives on CMIP5 model performance in the Nile River headwaters regions

Ranking the performance of global climate models (GCMs) is a notoriously difficult exercise. Multi-model comparison studies nearly always show that each model has strengths and weaknesses relative to others, and for many purposes the multi-model ensemble mean delivers better estimates than any individual model. Nevertheless, in regions like East Africa, where there is little consensus between models on the magnitude or sign of 21st century precipitation change, the multi-model ensemble mean approach to climate projection provides little value for adaptation planning. Here, we consider several possible frameworks for model evaluation and ranking, and assess the differences in performance of a subset of models participating in the 5th Coupled Model Intercomparison Project (CMIP5) according to each framework. Our test case is precipitation in the Nile River headwaters regions. We find that there is little consistency in the relative performance of models across frameworks based on amount and seasonality of precipitation, interannual precipitation variability, precipitation teleconnections, and continental scale climate patterns. These analyses offer some guidance on which GCMs are most likely to provide meaningful results for specific applications, but they caution that any effort to select 'best performing' GCMs for the Nile River basin must carefully consider the purposes for which GCMs are being selected.

Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models

Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change.

Projections of future meteorological drought and wet periods in the Amazon

Future intensification of Amazon drought resulting from climate change may cause increased fire activity, tree mortality, and emissions of carbon to the atmosphere across large areas of Amazonia. To provide a basis for addressing these issues, we examine properties of recent and future meteorological droughts in the Amazon in 35 climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that the CMIP5 climate models, as a group, simulate important properties of historical meteorological droughts in the Amazon. In addition, this group of models reproduces observed relationships between Amazon precipitation and regional sea surface temperature anomalies in the tropical Pacific and the North Atlantic oceans. Assuming the Representative Concentration Pathway 8.5 scenario for future drivers of climate change, the models project increases in the frequency and geographic extent of meteorological drought in the eastern Amazon, and the opposite in the West. For the region as a whole, the CMIP5 models suggest that the area affected by mild and severe meteorological drought will nearly double and triple, respectively, by 2100. Extremes of wetness are also projected to increase after 2040. Specifically, the frequency of periods of unusual wetness and the area affected by unusual wetness are projected to increase after 2040 in the Amazon as a whole, including in locations where annual mean precipitation is projected to decrease. Our analyses suggest that continued emissions of greenhouse gases will increase the likelihood of extreme events that have been shown to alter and degrade Amazonian forests.

Assessment of future changes in water availability and aridity

Substantial changes in the hydrological cycle are projected for the 21st century, but these projections are subject to major uncertainties. In this context, the "dry gets drier, wet gets wetter" (DDWW) paradigm is often used as a simplifying summary. However, recent studies cast doubt on the validity of the paradigm and also on applying the widely used P - E (precipitation - evapotranspiration) metric over global land surfaces. Here we show in a comprehensive CMIP5-based assessment that projected changes in mean annual P - E are generally not significant, except for high-latitude regions showing wetting conditions until the end of the 21st century. Significant increases in aridity do occur in many subtropical and also adjacent humid regions. However, combining both metrics still shows that approximately 70% of all land area will not experience significant changes. Based on these findings, we conclude that the DDWW paradigm is generally not confirmed for projected changes in most land areas.

Urgent need for warming experiments in tropical forests

Although tropical forests account for only a fraction of the planet's terrestrial surface, they exchange more carbon dioxide with the atmosphere than any other biome on Earth, and thus play a disproportionate role in the global climate. In the next 20 years, the tropics will experience unprecedented warming, yet there is exceedingly high uncertainty about their potential responses to this imminent climatic change. Here, we prioritize research approaches given both funding and logistical constraints in order to resolve major uncertainties about how tropical forests function and also to improve predictive capacity of earth system models. We investigate overall model uncertainty of tropical latitudes and explore the scientific benefits and inevitable trade-offs inherent in large-scale manipulative field experiments. With a Coupled Model Intercomparison Project Phase 5 analysis, we found that model variability in projected net ecosystem production was nearly 3 times greater in the tropics than for any other latitude. Through a review of the most current literature, we concluded that manipulative warming experiments are vital to accurately predict future tropical forest carbon balance, and we further recommend the establishment of a network of comparable studies spanning gradients of precipitation, edaphic qualities, plant types, and/or land use change. We provide arguments for long-term, single-factor warming experiments that incorporate warming of the most biogeochemically active ecosystem components (i.e. leaves, roots, soil microbes). Hypothesis testing of underlying mechanisms should be a priority, along with improving model parameterization and constraints. No single tropical forest is representative of all tropical forests; therefore logistical feasibility should be the most important consideration for locating large-scale manipulative experiments. Above all, we advocate for multi-faceted research programs, and we offer arguments for what we consider the most powerful and urgent way forward in order to improve our understanding of tropical forest responses to climate change.

Anthropogenic warming has increased drought risk in California

California is currently in the midst of a record-setting drought. The drought began in 2012 and now includes the lowest calendar-year and 12-mo precipitation, the highest annual temperature, and the most extreme drought indicators on record. The extremely warm and dry conditions have led to acute water shortages, groundwater overdraft, critically low streamflow, and enhanced wildfire risk. Analyzing historical climate observations from California, we find that precipitation deficits in California were more than twice as likely to yield drought years if they occurred when conditions were warm. We find that although there has not been a substantial change in the probability of either negative or moderately negative precipitation anomalies in recent decades, the occurrence of drought years has been greater in the past two decades than in the preceding century. In addition, the probability that precipitation deficits co-occur with warm conditions and the probability that precipitation deficits produce drought have both increased. Climate model experiments with and without anthropogenic forcings reveal that human activities have increased the probability that dry precipitation years are also warm. Further, a large ensemble of climate model realizations reveals that additional global warming over the next few decades is very likely to create ∼ 100% probability that any annual-scale dry period is also extremely warm. We therefore conclude that anthropogenic warming is increasing the probability of co-occurring warm-dry conditions like those that have created the acute human and ecosystem impacts associated with the "exceptional" 2012-2014 drought in California.

The Southern Ocean in the Coupled Model Intercomparison Project phase 5

The Southern Ocean is an important part of the global climate system, but its complex coupled nature makes both its present state and its response to projected future climate forcing difficult to model. Clear trends in wind, sea-ice extent and ocean properties emerged from multi-model intercomparison in the Coupled Model Intercomparison Project phase 3 (CMIP3). Here, we review recent analyses of the historical and projected wind, sea ice, circulation and bulk properties of the Southern Ocean in the updated Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble. Improvements to the models include higher resolutions, more complex and better-tuned parametrizations of ocean mixing, and improved biogeochemical cycles and atmospheric chemistry. CMIP5 largely reproduces the findings of CMIP3, but with smaller inter-model spreads and biases. By the end of the twenty-first century, mid-latitude wind stresses increase and shift polewards. All water masses warm, and intermediate waters freshen, while bottom waters increase in salinity. Surface mixed layers shallow, warm and freshen, whereas sea ice decreases. The upper overturning circulation intensifies, whereas bottom water formation is reduced. Significant disagreement exists between models for the response of the Antarctic Circumpolar Current strength, for reasons that are as yet unclear.

Projected changes in African easterly wave intensity and track in response to greenhouse forcing

Synoptic-scale African easterly waves (AEWs) impact weather throughout the greater Atlantic basin. Over the African continent, AEWs are instrumental in initiating and organizing precipitation in the drought-vulnerable Sahel region. AEWs also serve as the precursors to the most intense Atlantic hurricanes, and contribute to the global transport of Saharan dust. Given the relevance of AEWs for the climate of the greater Atlantic basin, we investigate the response of AEWs to increasing greenhouse gas concentrations. Using an ensemble of general circulation models, we find a robust increase in the strength of the winds associated with AEWs along the Intertropical Front in West Africa by the late 21st century of the representative concentration pathway 8.5. AEW energy increases directly due to an increase in baroclinicity associated with an enhanced meridional temperature gradient between the Sahara and Guinea Coast. Further, the pattern of low-level warming supports AEW development by enhancing monsoon flow, resulting in greater convergence and uplift along the Intertropical Front. These changes in energetics result in robust increases in the occurrence of conditions that currently produce AEWs. Given relationships observed in the current climate, such changes in the location of AEW tracks and the magnitude of AEW winds carry implications for the relationship between AEWs and precipitation in the Sahel, the mobilization of Saharan dust, and the likelihood of cyclogenesis in the Atlantic. Our results therefore suggest that changes in AEW characteristics could play a critical role in shaping the response of Atlantic basin climate to future increases in greenhouse gas concentrations.

Vegetation controls on northern high latitude snow-albedo feedback: observations and CMIP5 model simulations

The snow-masking effect of vegetation exerts strong control on albedo in northern high latitude ecosystems. Large-scale changes in the distribution and stature of vegetation in this region will thus have important feedbacks to climate. The snow-albedo feedback is controlled largely by the contrast between snow-covered and snow-free albedo (Δα), which influences predictions of future warming in coupled climate models, despite being poorly constrained at seasonal and century time scales. Here, we compare satellite observations and coupled climate model representations of albedo and tree cover for the boreal and Arctic region. Our analyses reveal consistent declines in albedo with increasing tree cover, occurring south of latitudinal tree line, that are poorly represented in coupled climate models. Observed relationships between albedo and tree cover differ substantially between snow-covered and snow-free periods, and among plant functional type. Tree cover in models varies widely but surprisingly does not correlate well with model albedo. Furthermore, our results demonstrate a relationship between tree cover and snow-albedo feedback that may be used to accurately constrain high latitude albedo feedbacks in coupled climate models under current and future vegetation distributions.