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

Heatwave attribution based on reliable operational weather forecasts

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2-50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10-50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Recent and projected changes in climate patterns in the Middle East and North Africa (MENA) region

Observational and reanalysis datasets reveal a northward shift of the convective regions over northern Africa in summer and an eastward shift in winter in the last four decades, with the changes in the location and intensity of the thermal lows and subtropical highs also modulating the dust loading and cloud cover over the Middle East and North Africa region. A multi-model ensemble from ten models of the Coupled Model Intercomparison Project-sixth phase gives skillful simulations when compared to in-situ measurements and generally captures the trends in the ERA-5 data over the historical period. For the most extreme climate change scenario and towards the end of the twenty-first century, the subtropical highs are projected to migrate poleward by 1.5°, consistent with the projected expansion of the Hadley Cells, with a weakening of the tropical easterly jet in the summer by up to a third and a strengthening of the subtropical jet in winter typically by 10% except over the eastern Mediterranean where the storm track is projected to shift polewards. The length of the seasons is projected to remain about the same, suggesting the warming is likely to be felt uniformly throughout the year.

Unveiling the role of climate in spatially synchronized locust outbreak risks

Desert locusts threaten crop production and food security. Spatially synchronized locust outbreaks further exacerbate these crises. Continental-scale understanding of such compound locust risks and underlying climatic drivers is crucial to designing coordinated and predictive control strategies but remains elusive. Here, we develop a data-driven framework to assess the compound risk of locust outbreaks in the Middle East and North Africa (MENA) and elucidate the role of climate in locust dynamics. We find that more than one-fifth of high-risk country pairs faced spatially synchronized locust risks from 1985 to 2020, dominated by concurrent winds or inundations. Individual locusts are more prone to infest arid areas punched by extreme rainfall. The spatial prevalence of locusts is strongly modulated by climate variability such as El Niño-Southern Oscillation. A warming climate will lead to widespread increases in locust outbreaks with emerging hotspots in west central Asia, posing additional challenges to the global coordination of locust control.

Future habitat changes of Bactrocera minax Enderlein along the Yangtze River Basin using the optimal MaxEnt model

Background

Bactrocera minax (Enderlein, 1920) (Diptera: Tephritidae) is a destructive citrus pest. It is mainly distributed throughout Shaanxi, Sichuan, Chongqing, Guizhou, Yunnan, Hubei, Hunan, and Guangxi in China and is considered to be a second-class pest that is prohibited from entering that country. Climate change, new farming techniques, and increased international trade has caused the habitable area of this pest to gradually expand. Understanding the suitable habitats of B. minax under future climate scenarios may be crucial to reveal the expansion pattern of the insect and develop corresponding prevention strategies in China.

Methods

Using on the current 199 distribution points and 11 environmental variables for B. minax, we chose the optimal MaxEnt model to screen the dominant factors that affect the distribution of B. minax and to predict the potential future distribution of B. minax in China under two shared socio-economic pathways (SSP1-2.6, SSP5-8.5).

Results

The current habitat of B. minax is located at 24.1-34.6°N and 101.1-122.9°E, which encompasses the provinces of Guizhou, Sichuan, Hubei, Hunan, Chongqing, and Yunnan (21.64 × 104 km2). Under future climate scenarios, the potential suitable habitat for B. minax may expand significantly toward the lower-middle reaches of the Yangtze River. The land coverage of highly suitable habitats may increase from 21.64 × 104 km2 to 26.35 × 104 × 104 km2 (2050s, SSP5-8.5) ~ 33.51 × 104 km2 (2090s, SSP5-8.5). This expansion area accounts for 29% (2050s, SSP1-2.6) to 34.83% (2090s, SSP1-2.6) of the current habitat. The center of the suitable habitat was predicted to expand towards the northeast, and the scenario with a stronger radiative force corresponded to a more marked movement of the center toward higher latitudes. A jackknife test showed that the dominant variables affecting the distribution of B. minax were the mean temperature of the driest quarter (bio9), the annual precipitation (bio12), the mean diurnal range (bio2), the temperature annual range (bio7), and the altitude (alt).

Discussion

Currently, it is possible for B. minax to expand its damaging presence. Regions with appropriate climate conditions and distribution of host plants may become potential habitats for the insects, and local authorities should strengthen their detection and prevention strategies. Climate changes in the future may promote the survival and expansion of B. minax species in China, which is represented by the significant increase of suitable habitats toward regions of high altitudes and latitudes across all directions but with some shrinkage in the east and west sides.

Frequency dominates intensity of future heat waves over India

Future changes in heat wave characteristics over India have been analyzed using Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) regional climate model simulations for mid-term (2041-2060) and long-term (2081-2099) future under the representative concentration pathway (RCP) 4.5 and RCP 8.5 emission scenarios, respectively. SMHI_CSIRO-MK3.6 was found to be the best model in simulating heat wave trend over India for historical period. Future projections show a four-to-seven-fold increase in heat wave frequency for mid-term and long-term future under RCP 4.5 scenario, and five-to-ten-fold increase under RCP 8.5 scenario with increase in frequency dominating intensity in both the scenarios. Northwestern, Central, and South-central India emerged as future heat wave hotspots with largest increase in the south-central region. This high-resolution regional future projection of heat wave occurrence will serve as a baseline for developing transformational heat-resilient policies and adaptation measures to reduce potential impact on human health, agriculture, and infrastructure.

Projected changes in atmospheric moisture transport contributions associated with climate warming in the North Atlantic

Global warming and associated changes in atmospheric circulation patterns are expected to alter the hydrological cycle, including the intensity and position of moisture sources. This study presents predicted changes for the middle and end of the 21st century under the SSP5-8.5 scenario for two important extratropical moisture sources: the North Atlantic Ocean (NATL) and Mediterranean Sea (MED). Changes over the Iberian Peninsula-considered as a strategic moisture sink for its location-are also studied in detail. By the end of the century, moisture from the NATL will increase precipitation over eastern North America in winter and autumn and on the British Isles in winter. Moisture from the MED will increase precipitation over the southern and western portions of the Mediterranean continental area. Precipitation associated with the MED moisture source will decrease mainly over eastern Europe, while that associated with the NATL will decrease over western Europe and Africa. Precipitation recycling on the Iberian Peninsula will increase in all seasons except summer for mid-century. Climate change, as simulated by CESM2 thus modifies atmospheric moisture transport, affecting regional hydrological cycles.

Climate change will accelerate the high-end risk of compound drought and heatwave events

Compound drought and heatwave (CDHW) events have garnered increased attention due to their significant impacts on agriculture, energy, water resources, and ecosystems. We quantify the projected future shifts in CDHW characteristics (such as frequency, duration, and severity) due to continued anthropogenic warming relative to the baseline recent observed period (1982 to 2019). We combine weekly drought and heatwave information for 26 climate divisions across the globe, employing historical and projected model output from eight Coupled Model Intercomparison Project 6 GCMs and three Shared Socioeconomic Pathways. Statistically significant trends are revealed in the CDHW characteristics for both recent observed and model simulated future period (2020 to 2099). East Africa, North Australia, East North America, Central Asia, Central Europe, and Southeastern South America show the greatest increase in frequency through the late 21st century. The Southern Hemisphere displays a greater projected increase in CDHW occurrence, while the Northern Hemisphere displays a greater increase in CDHW severity. Regional warmings play a significant role in CDHW changes in most regions. These findings have implications for minimizing the impacts of extreme events and developing adaptation and mitigation policies to cope with increased risk on water, energy, and food sectors in critical geographical regions.

The footprint of human-induced climate change on heat-related deaths in the summer of 2022 in Switzerland

Human-induced climate change is leading to an increase in the intensity and frequency of extreme weather events, which are severely affecting the health of the population. The exceptional heat during the summer of 2022 in Europe is an example, with record-breaking temperatures only below the infamous 2003 summer. High ambient temperatures are associated with many health outcomes, including premature mortality. However, there is limited quantitative evidence on the contribution of anthropogenic activities to the substantial heat-related mortality observed in recent times. Here we combined methods in climate epidemiology and attribution to quantify the heat-related mortality burden attributed to human-induced climate change in Switzerland during the summer of 2022. We first estimated heat-mortality association in each canton and age/sex population between 1990 and 2017 in a two-stage time-series analysis. We then calculated the mortality attributed to heat in the summer of 2022 using observed mortality, and compared it with the hypothetical heat-related burden that would have occurred in absence of human-induced climate change. This counterfactual scenario was derived by regressing the Swiss average temperature against global mean temperature in both observations and CMIP6 models. We estimate 623 deaths [95% empirical confidence interval (95% eCI): 151-1068] due to heat between June and August 2022, corresponding to 3.5% of all-cause mortality. More importantly, we find that 60% of this burden (370 deaths [95% eCI: 133-644]) could have been avoided in absence of human-induced climate change. Older women were affected the most, as well as populations in western and southern Switzerland and more urbanized areas. Our findings demonstrate that human-induced climate change was a relevant driver of the exceptional excess health burden observed in the 2022 summer in Switzerland.

Importance of humidity for characterization and communication of dangerous heatwave conditions

Heatwaves are one of the leading causes of climate-induced mortality. Using the examples of recent heatwaves in Europe, the United States and Asia, we illustrate how the communication of dangerous conditions based on temperature maps alone can lead to insufficient societal perception of health risks. Comparison of maximum daily values of temperature with physiological heat stress indices accounting for impacts of both temperature and humidity, illustrates substantial differences in geographical extent and timing of their respective peak values during these recent events. This signals the need to revisit how meteorological heatwaves and their expected impacts are communicated. Close collaboration between climate and medical communities is needed to select the best heat stress indicators, establish them operationally, and introduce them to the public. npj Climate and Atmospheric Science (2023) 6:33.

Quantifying the role of variability in future intensification of heat extremes

Heat extremes have grown disproportionately since the advent of industrialization and are expected to intensify further under unabated greenhouse warming, spreading unevenly across the globe. However, amplification mechanisms are highly uncertain because of the complex interplay between regional physical responses to human forcing and the statistical properties of atmospheric temperatures. Here, focusing on the latter, we explain how and to what extent the leading moments of thermal distributions sway the future trajectories of heat extremes. Crucially, we show that daily temperature variability is the key to understanding global patterns of change in the frequency and severity of the extremes and their exacerbation in many places. Variability accounts for at least half of the highly differential regional sensitivities and may well outweigh the background warming. These findings provide fundamental insights for assessing the reliability of climate models and improving their future projections.

Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP)

Extreme temperature and precipitation events are the primary triggers of hazards, such as heat waves, droughts, floods, and landslides, with localized impacts. In this sense, the finer grids of Earth System models (ESMs) could play an essential role in better estimating extreme climate events. The performance of High Resolution Model Intercomparison Project (HighResMIP) models is evaluated using the Expert Team on Climate Change Detection and Indices (ETCCDI) over the 1981-2014 period and future changes (2021-2050) under Shared Socioeconomic Pathway SSP5-8.5, over ten regions in Latin America and the Caribbean. The impact of increasing the horizontal resolution in estimating extreme climate variability on a regional scale is first compared against reference gridded datasets, including reanalysis, satellite, and merging products. We used three different groups based on the resolution of the model's grid (sg): (i) low (0.8° ≤ sg ≤ 1.87°), (ii) intermediate (0.5° ≤ sg ≤ 0.7°), and (iii) high (0.23° ≥ sg ≤ 0.35°). Our analysis indicates that there was no clear evidence to support the posit that increasing horizontal resolution improves model performance. The ECMWF-IFS family of models appears to be a plausible choice to represent climate extremes, followed by the ensemble mean of HighResMIP in their intermediate resolution. For future climate, the projections indicate a consensus of temperature and precipitation climate extremes increase across most of the ten regions. Despite the uncertainties presented in this study, climate models have been and will continue to be an important tool for assessing risk in the face of extreme events.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41748-022-00337-7.

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979-2014), near future (2025-2060) and far future (2065-2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions.

Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository

The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. We present the Atlas chapter of Working Group I (WGI) as a test case. We describe the application of the FAIR principles in the Atlas, the challenges faced during its implementation, and those that remain for the future. We introduce the open source repository resulting from this process, including coding (e.g., annotated Jupyter notebooks), data provenance, and some aggregated datasets used in some figures in the Atlas chapter and its interactive companion (the Interactive Atlas), open to scrutiny by the scientific community and the general public. We describe the informal pilot review conducted on this repository to gather recommendations that led to significant improvements. Finally, a working example illustrates the re-use of the repository resources to produce customized regional information, extending the Interactive Atlas products and running the code interactively in a web browser using Jupyter notebooks.

Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models

Emulators of Earth System Models (ESMs) are complementary to ESMs by providing climate information at lower computational costs. Thus far, the emulation of spatially resolved climate extremes has only received limited attention, even though extreme events are one of the most impactful aspects of climate change. Here, we propose a method for the emulation of local annual maximum temperatures, with a focus on reproducing essential statistical properties such as correlations in space and time. We test different emulator configurations and find that driving the emulations with global mean surface temperature offers an optimal compromise between model complexity and performance. We show that the emulations can mimic the temporal evolution and spatial patterns of the underlying climate model simulations and are able to reproduce their natural variability. The general design and the good performance for annual maximum temperatures suggest that the proposed methodology can be applied to other climate extremes.

A weakened AMOC may prolong greenhouse gas-induced Mediterranean drying even with significant and rapid climate change mitigation

The Mediterranean is a projected hot spot for climate change, with significant warming and rainfall reductions. We use climate model ensembles to explore whether these Mediterranean rainfall declines could be reversed in response to greenhouse gas reductions. While the summer Mediterranean rainfall decline is reversed, winter rainfall continues to decline. The continued decline results from prolonged weakening of Atlantic Ocean poleward heat transport that combines with greenhouse gas reductions to cool the subpolar North Atlantic, inducing atmospheric circulation changes that favor continued Mediterranean drying. This is a potential “surprise” in the climate system, whereby changes in one component (Atlantic Ocean circulation) alter how another component (Mediterranean rainfall) responds to greenhouse gas reductions. Such surprises could complicate climate change mitigation efforts.

The Mediterranean region has been identified as a climate hot spot, with models projecting a robust warming and rainfall decline in response to increasing greenhouse gases. The projected rainfall decline would have impacts on agriculture and water resources. Can such changes be reversed with significant reductions in greenhouse gases? To explore this, we examine large ensembles of a high-resolution climate model with various future radiative forcing scenarios, including a scenario with substantial reductions in greenhouse gas concentrations beginning in the mid-21st century. In response to greenhouse gas reductions, the Mediterranean summer rainfall decline is reversed, but the winter rainfall decline continues. This continued winter rainfall decline results from a persistent atmospheric anticyclone over the western Mediterranean. Using additional numerical experiments, we show that the anticyclone and continued winter rainfall decline are attributable to greenhouse gas–induced weakening of the Atlantic Meridional Overturning Circulation (AMOC) that continues throughout the 21st century. The persistently weak AMOC, in concert with greenhouse gas reductions, leads to rapid cooling and sea ice growth in the subpolar North Atlantic. This cooling leads to a strong cyclonic atmospheric circulation anomaly over the North Atlantic subpolar gyre and, via atmospheric teleconnections, to the anticyclonic circulation anomaly over the Mediterranean. The failure to reverse the winter rainfall decline, despite substantial climate change mitigation, is an example of a “surprise” in the climate system. In this case, a persistent AMOC change unexpectedly impedes the reversibility of Mediterranean climate change. Such surprises could complicate pathways toward full climate recovery.

How decadal predictions entered the climate services arena: an example from the agriculture sector

Predicting the variations in climate for the coming 1-10 years is of great interest for decision makers, as this time horizon coincides with the strategic planning of stakeholders from climate-vulnerable sectors such as agriculture. This study attempts to illustrate the potential value of decadal predictions in the development of climate services by establishing interactions and collaboration with stakeholders concerned with food production and security. Building on our experience from interacting with users and the increased understanding of their needs gathered over the years through our participation in various European activities and initiatives, we developed a decadal forecast product that provides tailored and user-friendly information about multi-year dry conditions for the coming five years over global wheat harvesting regions. This study revealed that the coproduction approach, where the interaction between the user and climate service provider is established at an early stage of forecast product development, is a fundamental step to successfully provide useful and ultimately actionable information to the interested stakeholders. The study also provides insights that shed light on the reasons for the delayed entry of decadal predictions in the climate services discourse and practice, obtained from surveying climate scientists and discussing with decadal prediction experts. Finally, it shows the key challenges that this new source of climate information still faces.

Present-day and future projection of East Asian summer monsoon in Coupled Model Intercomparison Project 6 simulations

The East Asian summer monsoon (EASM) is an influential monsoon system that provides two-thirds of the annual precipitation in the Asian region. Therefore, considerable attention has been paid to the changes in future climate. Thus far, studies on EASM characteristics have not been conducted considering specific global warming level (GWL) using Coupled Model Inter-comparison Project 6 (CMIP6) simulations. We analyze the EASM characteristics in present-day (PD) and the changes in EASM corresponding to the projections at 1.5, 2.0, and 3.0°C GWLs. The newly released 30 CMIP6 models effectively captured the migration of the monsoon in PD with a pattern correlation coefficient of 0.91, which is an improvement over that reported in previous studies. As a result of the separate analysis of the P1 (first primary peak; 33–41 pentad) and P2 (from P1 to the withdrawal; 42–50 pentad) periods, a higher frequency of weak to moderate precipitation in P2 and a smaller amount of moderate to extreme precipitation in P1 are mainly occurred. The CMIP6 models project increasing precipitation of approximately 5.7%°C⁻¹, 4.0%°C⁻¹, and 3.9%°C⁻¹ for the three GWLs, respectively, with longer durations (earlier onset and delayed termination). Under the three GWLs, the projected precipitation frequency decreases below 6 mm d⁻¹ (76th percentile) and significant increases above 29 mm d⁻¹ (97th percentile). These changes in precipitation frequency are associated with an increasing distribution of precipitation amount above 97th percentile. Additionally, these tendencies in P1 and P2 are similar to that of the total period, while the maximum changes occur in 3.0°C GWL. In particular, future changes in EASM accelerate with continuous warming and are mainly affected by enhanced extreme precipitation (above 97th percentile). Our findings are expected to provide information for the implementation of sustainable water management programs as a part of national climate policy.

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes

The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.

Projection of Extreme Temperature Events over the Mediterranean and Sahara Using Bias-Corrected CMIP6 Models

Climate change continues to increase the intensity, frequency and impacts of weather and climate extremes. This work uses bias-adjusted Coupled Model Intercomparison Project Phase six (CMIP6) model datasets to investigate the future changes in temperature extremes over Mediterranean (MED) and Sahara (SAH) regions. The mid- (2041–2070) and far-future (2071–2100) are studied under two Shared Socioeconomic Pathways: SSP2-4.5 and SSP5-8.5 scenarios. Quantile mapping function greatly improved the performance of CMIP6 by reducing the notable biases to match the distribution of observation data, the Climate Prediction Center (CPC). Results show persistent significant warming throughout the 21st century, increasing with the increase in radiative forcing. The MED will record a higher increase in temperature extremes as compared to SAH. The warming is supported by the projected reduction in cold days (TX10p) and cold nights (TN10p), with the reduction in the number of cold nights exceeding cold days. Notably, warm spell duration index (WSDI) and summer days (SU) have a positive trend in both timelines over the entire study area. There is a need to simulate how climate sensitive sectors, such as water and agriculture, are likely to be affected by projected changes under different scenarios for informed decision making in the choice and implementation of adaptation and mitigation effective measures.

Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran

This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1-G8). Synoptic data for the period 1995-2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020-2040), middle future (2040-2060), and far future (2060-2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson's correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995-2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by -74%. The projected maximum temperature showed an increase up to +8°C in highland climate regions. The minimum temperature revealed an increase up to +4°C in the Zagros mountains and decreased by -4°C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate.

Increased high-temperature extremes and associated population exposure in Africa by the mid-21st century

Previous studies warned that heat extremes are likely to intensify and frequently occur in the future due to climate change. Apart from changing climate, the population's size and distribution contribute to the total changes in the population exposed to heat extremes. The present study uses the ensemble mean of global climate models from the Coupled Model Inter-comparison Project Phase six (CMIP6) and population projection to assess the future changes in high-temperature extremes and exposure to the population by the middle of this century (2041-2060) in Africa compared to the recent climate taken from 1991 to 2010. Two Shared Socioeconomic Pathways (SSPs), namely SSP2-4.5 and SSP5-8.5, are used. Changes in population exposure and its contributors are quantified at continental and for various sub-regions. The intensity of high-temperature extremes is anticipated to escalate between 0.25 to 1.8 °C and 0.6 to 4 °C under SSP2-4.5 and SSP5-8.5, respectively, with Sahara and West Southern Africa projected to warm faster than the rest of the regions. On average, warm days' frequency is also expected to upsurge under SSP2-4.5 (26-59%) and SSP5-8.5 (30-69%) relative to the recent climate. By the mid-21st century, continental population exposure is expected to upsurge by ~25% (28%) of the reference period under SSP2-4.5|SSP2 (SSP5-8.5|SSP5). The highest increase in exposure is expected in most parts of West Africa (WAF), followed by East Africa. The projected changes in continental exposure (~353.6 million person-days under SSP2-4.5|SSP2 and ~401.4 million person-days under SSP5-8.5|SSP5) are mainly due to the interaction effect. However, the climate's influence is more than the population, especially for WAF, South-East Africa and East Southern Africa. The study findings are vital for climate change adaptation.

Climate change risks pushing one-third of global food production outside the safe climatic space

Food production on our planet is dominantly based on agricultural practices developed during stable Holocene climatic conditions. Although it is widely accepted that climate change perturbs these conditions, no systematic understanding exists on where and how the major risks for entering unprecedented conditions may occur. Here, we address this gap by introducing the concept of safe climatic space (SCS), which incorporates the decisive climatic factors of agricultural production: precipitation, temperature, and aridity. We show that a rapid and unhalted growth of greenhouse gas emissions (SSP5-8.5) could force 31% of the global food crop and 34% of livestock production beyond the SCS by 2081-2100. The most vulnerable areas are South and Southeast Asia and Africa's Sudano-Sahelian Zone, which have low resilience to cope with these changes. Our results underpin the importance of committing to a low-emissions scenario (SSP1-2.6), whereupon the extent of food production facing unprecedented conditions would be a fraction.

The Climate Response to Emissions Reductions Due to COVID-19: Initial Results From CovidMIP

Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.