Multi-model Ensemble Projections Research Articles

CMIP6 multi-model ensemble projection of reference evapotranspiration using machine learning algorithms

Changes in reference crop evapotranspiration (ETo) due to climate change (CC) can severely impact food and water security, emphasizing the need for integrating ETo projections into agricultural water management strategies. In this study, ETo changes were projected for two future time slices with respect to the baseline using several machine learning techniques, incorporating minimum and maximum temperature, diurnal temperature range, and extraterrestrial radiation across Iran. Additionally, an ensemble of 10 CMIP6 Global Climate Models, downscaled by the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6), was employed. The X-means clustering algorithm was also exploited to classify ETo based on various characteristics, including minimum, maximum, average, skewness, and standard deviation, as well as ETo ranges of 0–5, 5–10, and greater than 10 mm d⁻¹. This clustering approach divided the study area into five distinct clusters. Apart from cluster I, where the Support Vector Machine outperformed, the Random Forest technique provided more accurate ETo predictions. The findings project an average ETo increase of 4.8 % and 5.3 % during 2030–2049, and 8.0 % and 13.3 % for 2080–2099 under SSP245 and SSP585, respectively. Geographically, the highest ETo increases are anticipated primarily in the northern and western parts of the country, predominantly within clusters I and II. Notably, the ETo rise will exceed 40 % relative to the baseline during the late century under the SSP585. Furthermore, the most significant ETo increment is expected during winter. Future projections also indicate that cluster V, which already experiences significant daily ETo peaks, will face even more ETo extremes. Given the critical importance of these regions for sustaining food and water security and preserving natural resources, the substantial rise in ETo under future CC poses a significant threat to natural sustainability in Iran. This highlights the critical necessity for adaptive strategies in agricultural water management to mitigate the adverse CC effects. In this context, the current findings can assist decision-makers in identifying hotspots and quantifying CC impacts, thereby enabling the design of crucial adaptations.

Estimating future changes in streamflow and suspended sediment load under CMIP6 multi-model ensemble projections: a case study of Bitlis Creek, Turkey

Estimating future changes in streamflow and suspended sediment load under CMIP6 multi-model ensemble projections: a case study of Bitlis Creek, Turkey

Unusually low dust activity in North Africa in June 2023: Causes, impacts and future projections

Dust activity during the pre-monsoon season in Africa has an impact on the monsoon circulation and the Atlantic hurricane season. During early June 2023 the atmosphere was relatively clear over West Africa and the eastern tropical Atlantic, in contrast with the dustier June 2020. The negative phase of the North Atlantic Oscillation suppressed dust lifting, with an equatorward shifted African Easterly Jet limiting its downstream advection. On the other hand, dust accumulated in the atmosphere over northeastern Africa, with the negative dust aerosol optical depth (DAOD) anomalies over western Africa and the positive anomalies over eastern Africa more than two standard deviations away from the climatological mean. The lack of dust led to an up to 55 W m−2 increase in the surface downward shortwave radiation flux and a 35 W m−2 decrease in the longwave flux, and is in line with the record-breaking sea surface temperatures over the eastern tropical Atlantic and the active start to the Atlantic hurricane season. In order to explore future projections of DAOD, a multi-model ensemble (MME) is constructed from 29 models that integrate the sixth phase of the Coupled Model Intercomparison Project (CMIP6). It captures the positive trend in the June DAOD over the eastern tropical Atlantic during 1980–2014, although the amplitude is roughly a factor of six smaller than the 0.0017 year−1 in the reanalysis dataset. The CMIP6 MME projects a further increase in DAOD in the region at a rate of up to 0.0003 year−1 in the most extreme climate change scenario for 2066–2100, which is comparable to that seen during the historical period, even though the mean values are projected to decrease by 0.03–0.06. While lower dust loadings may lead to improved air quality, they will likely further fuel pre-season and early season storms in the North Atlantic, which have become more frequent in recent decades.

Measuring climate vulnerability of tourism-dependent livelihoods: The case of Lamgtang National Park

Climate change poses significant challenges to tourism-dependent communities in mountain regions, threatening their livelihoods and well-being. However, there is a lack of comprehensive assessments that consider the multidimensional nature of vulnerability and the specific socio-cultural contexts of these communities. This study assesses the livelihood vulnerability of tourism-dependent communities in Lamtang National Park exposed to climate change using the Livelihood Vulnerability Index (LVI) and the Livelihood Vulnerability Index -Intergovernmental Panel on Climate Change (LVI-IPCC) framework. A mixed-methods approach was employed, including household surveys (n=119), vulnerability index calculations, and analysis of weather data. The purposive stratified sampling based on ecological gradient and proximity to trekking trails ensured the representativeness of the sample. One hundred nineteen households were surveyed, with respondents from the Hill Janajati ethnic group. Results showed that the majority of households (63%) were tourism-dominant, followed by agriculture-dominant (17%) and mixed livelihood (13%). The LVI results revealed a moderate vulnerability (0.365), with financial and natural capitals being the most vulnerable. The LVI-IPCC analysis showed that the community's adaptive capacity (0.537) is slightly lower than its exposure (0.564), and sensitivity is comparatively low (0.296), resulting in a low LVI-IPCC index (0.01). Weather data analysis, including the Mann-Kendall trend test, Sen's slope analysis, and multi-model ensemble projections, indicated increasing precipitation trends and a warmer, wetter future for the region. The triangulation of LVI, LVI-IPCC, forecast data, and weather station data strengthens the findings and highlights the need for targeted interventions. The projected changes in temperature and precipitation patterns for Rasuwa district and vulnerability status of tourism-dependent communities highlight the urgency of implementing climate change adaptation measures, which may include diversifying livelihoods, improving access to education and training, strengthening social support systems, and promoting sustainable land and water management practices.

Projected changes in heat, extreme precipitation, and their spatially compound events over China’s coastal lands and seas through a high-resolution climate models ensemble

China’s coastal lands and seas are highly susceptible to the changing environment due to their dense population and frequent economic activities. These areas experience more significant impacts from climate change-induced extreme events than elsewhere. The most noticeable effects of climate change are extreme high temperatures and extreme precipitation. We employ an ensemble of RCMs (Regional Climate Models) to investigate and project changes in temperature, precipitation, and Compound Heat-Precipitation Extreme events (CHPEs) over selected China’s coastal lands and seas for both historical (1985–2004) and future periods (2080–2099). The multi-model ensemble projects that daily temperature extremes will increase by 2.9 °C to 5.4 °C across China’s coastal lands and seas, with land areas showing a higher temperature increase than marine areas. Extreme precipitation shows a high geographical heterogeneity with a 2.8–3.9 mm d−1 reduction over the 15–25°N marine areas while a 2.2–5.4 mm d−1 increment over the 25°N-35°N land areas. We use the Clausius–Clapeyron relationship to reveal that the peak of daily extreme precipitation will increase by 2–7 mm d−1 and the temperature at which extreme precipitation peaks will increase by 2 °C to 6 °C by warming. The land area of 25–30°N has the highest peak precipitation increase of 9.87 mm d−1 and a peak temperature increase of 6 °C. As precipitation extremes intensify with daily temperature extremes increase, CHPEs are projected to occur more frequently over both land and marine areas. Compared with the historical period, the frequency of CHPEs will increase by 40.9%-161.2% over marine areas, and by 36.2%-163.6% over land areas in the future. The 15–20°N area has the highest frequency increase of CHPE events, and the 25–30°N area has the largest difference in frequency increase under two different scenarios. It indicated that the 25–30°N area will be more easily affected by climate change.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis

An unprecedented soil moisture drought broke out over the Yangtze River basin (YRB) in the summer of 2022 and lasted until the spring of 2023, caused great economic losses and serious environmental issues. With the rapid onset and long-lasting duration, the mega soil drought challenges the current seasonal prediction capacity. Whether the state-of-the-art climate models provide skillful predictions of the onset, persistence and recovery of the 2022–23 mega soil drought needs to be assessed. Identified by the drought area percentage, here we show that the mega soil drought over the YRB started from July, 2022, reached the peak in August, and diminished in April, 2023. Combined with real-time predictions of monthly precipitation released by three climate models participating in the North American multi-model ensemble (NMME) project, we predict the monthly evolution of the 2022–23 soil drought through a joint distribution between precipitation and soil moisture established by the copula method. The results indicate that the NMME/copula prediction well reproduced the spatiotemporal evolution of the mega soil drought at 1 month lead. Using the climatological prediction that relies on the information of initial soil moisture conditions as the reference forecast, the Brier skill score (BSS) values for NMME multi-model ensemble are 0.26, 0.23 and 0.2 for the forecast lead times increased from 1 to 3 months during the entire soil drought period. Specifically, the BSS is 0.14 at 2 months lead during drought onset stage, and 0.26 at 3 months lead during persistence stage, while it is close to zero at all leads during the recovery stage. Our study implies that climate models have great potential in probabilistic seasonal prediction of the onset and persistency of mega soil drought through combining with the copula method.

Ensemble projections of climate and streamflow in a typical basin of semi-arid steppes in Mongolian Plateau of 2021–2100

The Kherlen River is the main water source for Hulun Lake, the largest lake in northern China. Due to reduced inflow from the Kherlen River, Hulun Lake experienced rapid shrinkage at the beginning of the 21st century, posing a serious threat to the ecological security of northern China. However, there is still a significant lack of projections regarding future climate change and its hydrological response in the Kherlen River basin. This study analyzed the projected climate and streamflow changes in the Kherlen River basin, a vital yet vulnerable international semi-arid steppes type basin. A combination of multi-model ensemble projection techniques, and the soil and water assessment tool (SWAT) model was employed to examine the spatio‒temporal changes in precipitation, temperature, streamflow, and the associated uncertainties in the basin. The temperature (an increase of 1.84–6.42 °C) and the precipitation (an increase of 15.0–46.0 mm) of Kherlen River basin are projected to increase by 2100, leading to a rise in streamflow (1.08–4.78 m3 s−1). The upstream of the Kherlen River exhibits remarkable increasing trends in precipitation, which has a dominant influence on streamflow of Kherlen River. Noteworthy increases in streamflow are observed in April, August, September, and October compared to the reference period (1971–2000). These findings suggest a partial alleviation of water scarcity in the Kherlen River, but also an increased likelihood of hydrological extreme events. The projected temperature increase in the Kherlen River basin exhibits the smallest uncertainty, while more pronounced uncertainties are found in precipitation and streamflow. The spread among the results of CMIP6 models is greater than that of CMIP5 models, with lower signal-to-noise ratio (SNR) values for temperature, precipitation, and streamflow.

Quantifying the climate change impacts on the magnitude and timing of hydrological extremes in the Baro River Basin, Ethiopia

Extreme hydrological events, like floods and droughts, exert considerable effects on both human and natural systems. The frequency, intensity, and duration of these events are expected to change due to climate change, posing challenges for water resource management and adaptation. In this study, the Soil and Water Assessment Tool plus (SWAT +) model was calibrated and validated to simulate flow under future shared socioeconomic pathway (SSP2-4.5 and SSP5-8.5) scenarios in the Baro River Basin with R2 values of 0.88 and 0.83, NSE of 0.83 and 0.74, and PBIAS of 0.39 and 8.87 during calibration and validation. Six bias-corrected CMIP6 Global Climate Models (GCM) were selected and utilized to investigate the effects of climate change on the magnitude and timing of hydrological extremes. All climate model simulation results suggest a general increase in streamflow magnitude for both emission scenarios (SSP2-4.5 and SSP5-8.5). The multi-model ensemble projections show yearly flow increases of 4.8% and 12.4% during the mid-term (MT) (2041–2070) and long-term (LT) (2071–2100) periods under SSP2-4.5, and 15.7% and 35.6% under SSP5-8.5, respectively. Additionally, the analysis revealed significant shifts in the projected annual 1 day, 3 day, 7 day, and 30 day maximum flows, whereas the annual 3 day and 7 day minimum flow fluctuations do not present a distinct trend in the future scenario compared to the baseline (1985–2014). The study also evaluated the timing of hydrological extremes, focusing on low and peak flow events, utilizing the annual 7 day maximum and minimum flow for this analysis. An earlier occurrence was noted for both peak and low flow in the SSP2-4.5 scenario, while a later occurrence was observed in the SSP5-8.5 scenario compared to the baseline. In conclusion, this study showed the significant effect of climate change on river hydrology and extreme flow events, highlighting their importance for informed water management and sustainable planning.

Investigating the Effect of Climate Change on Drought Propagation in the Tarim River Basin Using Multi-Model Ensemble Projections

Recent studies on China’s arid and semi-arid regions, particularly the Tarim River Basin (TRB), have shown an increase in the intensity and frequency of extreme weather events. This research examines the link between meteorological droughts, as measured by the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts, as indicated by the Standardized Runoff Index (SRI) and the Standardized Terrestrial Water Storage Index (STI), over various time scales. Historical data indicate that SPEI drought frequency (DF) was 14.3–21.9%, with prevalent events in the northern oases. SRI DF ranged from 9.0% to 35.8%, concentrated around the Taklamakan and Kumtag Deserts, while STI DF varied between 4.4% and 32.7%, averaging 15% basin-wide. Future projections show an increased DF of SPEI in deserts and a decrease in oases; SRI DF decreased in deserts but increased in oases. STI changes were more moderate. The study also found a higher risk of drought progression from SPEI to SRI in the southwestern and northeastern oases, exceeding 50% probability, while central and eastern TRB had lower risks. The western TRB and inner Taklamakan Desert faced higher risks of SPEI to STI progression, with probabilities over 45%, in contrast to the lower risks in the eastern and central oases. The concurrence of SRI/STI with moderate to extreme SPEI droughts led to a higher probability and area of SRI/STI droughts, whereas consistent SPEI types showed a reduced induced probability and extent of SRI/STI droughts. This study enhances the understanding of drought propagation from meteorological to hydrological droughts in the TRB and contributes to the prevention of hydrological drought to a certain extent.

Reconciling roles of the South China Sea summer monsoon and ENSO in prediction of the Indian Ocean dipole

The Indian Ocean dipole (IOD) is a remarkable interannual variability in the tropical Indian Ocean. The improved prediction of IOD is of a great value because of its large socioeconomic impacts. Previous studies reported that both El Niño-Southern Oscillation (ENSO) and South China Sea summer monsoon (SM) play a dominant role in the western and eastern pole of the IOD, respectively. They can be used as predictors of the IOD at 3 month lead beyond self-persistence. Here, we develop an empirical model of multi-factors in which the western pole is predicted by ENSO and persistence and the eastern pole is predicted by SM and persistence. This new empirical model outperforms largely the average level of the dynamical models from the North American multi-model ensemble (NMME) project in predicting the peak IOD in boreal autumn, with a correlation coefficient of ∼0.86 and a root mean square error of ∼0.24 °C. Furthermore, the hit rate of positive culminated IOD in this new empirical model is equivalent to that in current NMME models (above 65%), much higher than that for negative culminated IOD. This improvement of skill using the empirical model suggests a perspective for better understanding and predicting the IOD.

Partitioning model uncertainty in multi-model ensemble river flow projections

Floods are the largest natural disaster currently facing the UK, whilst the incidents of droughts have increased in recent years. Floods and droughts can have devastating consequences on society, resulting in significant financial damage to the economy. Climate models suggest that precipitation and temperature changes will exacerbate future hydrological extremes (i.e., floods and droughts). Such events are likely to become more frequent and intense in the future; thus to develop adaptation plans climate model projections feed hydrological models to provide future water resource projections. ‘eFLaG’ is one set of future river flow projections produced for the UK driven by UKCP18 climate projections from the UK Met Office. The UKCP18-derived eFLaG dataset provides state-of-the-art projections for a single GCM driven by RCP 8.5 across the entire UK. A QE-ANOVA approach has been used to partition contributing sources of uncertainty for two flow quantiles (Q5 high flows and Q95 low flows), at near and far future time scales, for each of the 186 GB catchments in the eFLaG dataset. Results suggest a larger hydrological model uncertainty associated with low flows and greater regional climate model uncertainty for high flows which remains stationary between flow indicators. Total uncertainty increases from near to far future and highly uncertain catchments have been identified with a high concentration in South-East England.

Bayesian weighting of climate models based on climate sensitivity

Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 oC for a low emissions scenario (SSP1-2.6) and 5 oC for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.

Projected changes in rainfall amount and distribution in the Democratic Republic of Congo – Evidence from an ensemble of high-resolution climate simulations

Extreme precipitation has been threatening many sectors of human society and is likely to intensify with global warming. In the present study, we have analysed the impact of future climate change on daily mean rainfall and heavy rainfall on seasonal and annual scales in the Democratic Republic of Congo (DRC) by using ten long-term runs of high-resolution regional climate models. We initially assessed the performance of each run and the multi-model ensemble mean in simulating the daily mean rainfall (PRE), the maximum one-day rainfall (Rx1day), the simple rainfall intensity index (SDII), the count of days when rainfall is greater than or equal to 20 mm (RR20mm), the total rainfall when the daily rainfall exceeds the 95th percentile of the wet-day rainfall (R95p) and the maximum number of consecutive days with more than or equal to 1 mm (CWD). Next, we examined the future changes of these indices, focusing on the multi-model ensemble mean and spread under the high emission scenario RCP8.5. The time frames considered are the mid and end of the twenty-first century (2035–2065 and 2070–2100, respectively). The results indicate that the performance of REMO2015-MPI-ESM was close to the multi-model ensemble mean in representing the mean rainfall and most heavy rainfall indices, while CanRCM4-CamESM2 was identified as the worst performing model. The key finding of this study is that the multi-model ensemble mean project no significant change regarding the daily mean rainfall throughout the year and across all seasons by the middle of the 21st century, except of the western region of the country where a decrease is projected. Simultaneously, Rx1day, SDII, RR20mm, and R95p are projected to decrease almost everywhere during all seasons. Moreover, a consistent decrease in the number of wet days is projected. Focusing on the end of the century, the multi-model ensemble mean project an overall decrease in daily mean rainfall, especially in the western region of the DRC, with a more pronounced effect during MAM. The increase in Rx1day, SDII, RR20mm, and R95p, along with a decrease in CWD, is amplified in this period. These findings are useful for predicting the potential threats of precipitation-related diseases and natural hazards, as well as for designing climate-resilient infrastructure and socioeconomic activities in the DRC.

Quantifying climate change impacts on hydropower production under CMIP6 multi-model ensemble projections using SWAT model

ABSTRACT This study assesses the effects of climate change on hydropower production in the most threatened highlands region of the Euphrates-Tigris Basin, with the case of the Dipni Project. This evaluation is based on the precipitation and temperature predictions of the multi-model ensembles produced by analysing the simulations of 24 global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Soil and Water Assessment Tool (SWAT) model is utilized to estimate the future inflow rates of the Dipni reservoir under the Shared Socio-economic Pathway (SSP) scenarios of SSP245 and SSP585. The 25-year reservoir operations conducted in the past and three future periods indicate possible decreases of up to 10.1% and 21.5% in the annual energy production under the SSP245 and SSP585 scenarios, respectively. The results show the need to take adaptive measures against the projected impacts of climate change to achieve the targeted return for the coming decades.

A clustering-based multi-model ensemble projection of near-term precipitation changes over East China and its uncertainty

An ensemble of 28 models from the 6th Coupled Model Intercomparison Project was used to project future changes in annual mean precipitation over East China during 2046–2065 relative to 1995–2014 under the SSP2-4.5 scenario. A precipitation increase of 0.16 mm d−1 is projected over North China by the multi-model ensemble (MME) mean. However, large model uncertainty exists over South China (SC), reducing the fidelity of the MME mean projection. Thus, a clustering-based MME probabilistic projection is presented that projects four possible SC precipitation changes in the future. Precipitation increases are projected in Cluster 1, Cluster 3 and Cluster 4 for 0.51 mm d−1, 0.2 mm d−1 and 0.23 mm d−1, respectively, with occurrence probabilities of 14.3%, 35.7% and 25%, respectively. Conversely, the projected Cluster 2 precipitation decrease is 0.01 mm d−1 with an occurrence probability of 25%. The differences in precipitation change are mainly contributed by dynamic effect due to different circulation changes across clusters. During extended summer, different circulation anomalies over western North Pacific (WNP) among clusters arise from the sea surface temperature anomaly (SSTA) warming patterns over the equatorial central-eastern Pacific, which explain the different precipitation increases over SC. During extended winter, a strong zonal SSTA gradient between the South China Sea and adjacent WNP is projected in Cluster 2, stimulating a zonally vertical cell with anomalous descent over SC and resulting in markedly decreased precipitation. A similar but much weaker zonal SSTA gradient and circulation anomaly are projected in Cluster 3. Distinct meridional SSTA gradients over the WNP are projected in the rest clusters, stimulating shifted descents with a weak effect on SC precipitation.

Multimodel ensemble projection of photovoltaic power potential in China by the 2060s

China's demand for solar energy has been growing rapidly to meet energy transformation targets. However, the potential of solar energy is affected by weather conditions and is expected to change under climate warming. Here, the authors project the photovoltaic (PV) power potential over China under low and high emission scenarios by the 2060s, taking advantage of meteorological variables from 24 CMIP6 models and 4 PV models with varied formats. The ensemble mean of these models yields an average PV power of 277.2 KWh m−2 yr−1 during 2004–2014, with a decreasing tendency from the west to east. By 2054–2064, the national average PV power potential is projected to increase by 2.29% under a low emission scenario but decrease by 0.43% under a high emission scenario. The emission control in the former scenario significantly enhances surface solar radiation and promotes PV power in the east. On the contrary, strong warming causes inhibitions to PV power generation under the high emission scenario. Extreme warming events on average decrease the PV power potential by 0.28% under the low emission scenario and 0.44% under the high emission scenario, doubling and tripling the present-day loss, respectively. The projections reveal large benefits of controlling emissions for the future solar energy in China due to both the clean atmosphere and the moderate warming.

Multi-model ensemble projection of the global dust cycle by the end of 21st century using the Coupled Model Intercomparison Project version 6 data

Abstract. As a natural aerosol with the largest emissions on land, dust has important impacts on the atmospheric environment and climate systems. Both the emissions and transport of dust aerosols are tightly connected to meteorological conditions and as a result are confronted with strong modulations by the changing climate. Here, we project the changes in the global dust emissions and loading by the end of the 21st century, using an ensemble of model outputs from the Coupled Model Intercomparison Project version 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs). Based on the validations against site-level observations, we select 9 out of 14 models and estimate an ensemble global dust emissions of 2566 ± 1996 Tg a−1 (1 Tg = 1012 g) for the present day, in which 68 % is dry deposited and 31 % is wet deposited. Compared to 2005–2014, global dust emissions show varied responses, with a reduction of −5.6 ± 503 Tg a−1 under the SSP3–7.0 scenario but increased emissions up to 60.7 ± 542 Tg a−1 under the SSP5–8.5 scenario at 2090–2099. For all scenarios, the most significant increase in the dust emissions appears in North Africa (0.6 %–5.6 %) due to the combined effects of reduced precipitation but strengthened surface wind. In contrast, all scenarios show decreased emissions in the Taklimakan and Gobi deserts (−0.8 % to −11.9 %) because of the increased precipitation but decreased wind speed on a regional scale. The dust loading shows uniform increases over North Africa (1.6 %–13.5 %) and the downwind Atlantic, following the increased emissions but decreases over East Asia (−1.3 % to −10.5 %), and the downwind Pacific, partly due to enhanced local precipitation that promotes wet deposition. In total, global dust loading will increase by 2.0 %–12.5 % at the end of the 21st century under different climate scenarios, suggesting a likelihood of strengthened radiative and climatic perturbations by dust aerosols in a warmer climate.

Longevity of the whitefly parasitoid Eretmocerus eremicus under two different climate scenarios

Whiteflies (Aleyrodidae) cause high economic losses in agricultural systems worldwide. Heavy reliance on insecticide use for whitefly control has led to the resistance development towards nearly all used groups of insecticides. A more sustainable, widely used, and irreplaceable control measure in protected cropping systems is biological control by augmentative release of parasitoids. All commercially available whitefly parasitoids are wasps from the genera Encarsia and Eretmocerus, with one of the most used parasitoid species being Eretmocerus eremicus. Biocontrol by these highly specialized natural enemies is sensitive to changes in environmental conditions. Ongoing anthropogenic climate change could affect multitrophic interactions between organisms, and biocontrol systems are not an exception. At the same time, little is known about the development of E. eremicus under projected future climate conditions. The present study evaluates the longevity of this important biocontrol agent by performing climatic chamber simulation driven by physically consistent, regionally downscaled, multi-model ensemble projections of the future climate for Luxembourg. Results show a reduction of its longevity up to 50% under future climate. The median survival in the projected future climate was found to be 13 days, which is 9 days less than under present climate. Implications on the efficacy of the whitefly biocontrol practices in future climate conditions are discussed.

Avoidable heat risk under scenarios of carbon neutrality by mid-century

To prevent anthropogenic warming of the climate system above dangerous thresholds, governments are required by the Paris Agreement to peak global anthropogenic CO2 emissions and to reach a net zero CO2 emissions level (also known as carbon neutrality). Growing concerns are being expressed about the increasing heat stress caused by the interaction of changes in temperature and humidity in the context of global warming. Although much effort has been made to examine future changes in heat stress and associated risks, gaps remain in understanding the quantitative benefits of heat-risk avoidance from carbon-neutral policies, limited by the traditional climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Here we quantify the avoided heat risk during 2040–2049 under two scenarios of global carbon neutrality by 2060 and 2050, i.e., moderate green (MODGREEN) and strong green (STRGREEN) recovery scenarios, relative to the baseline scenario (FOSSIL), based on multi-model large ensemble climate projections from a new climate model intercomparison project (CovidMIP) that endorsed by CMIP6. We show that global population exposure to extreme heat stress increases by approximately four times its current level during 2040–2049 under the FOSSIL scenario, whereas the heat exposure could be reduced by as much as 12 % and 23 % under the MODGREEN and STRGREEN scenarios, respectively. Moreover, global mean heat-related mortality risk is mitigated by 14 % (24 %) under the MODGREEN (STRGREEN) scenario during 2040–2049 relative to the FOSSIL scenario. Additionally, the aggravating heat risk could be mitigated by around a tenth by achieving carbon neutrality 10 years earlier (2050 versus 2060). In terms of spatial pattern, this heat-risk avoidance from low-carbon policies is typically greater in low-income countries. Our findings assist governments in advancing early climate change mitigation policy-making.