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Animal coloration serves a variety of visually related functions in nature (e.g. mate choice, aposematism and camouflage) but the pigments in integumentary tissues such as skin, scales and feathers may also serve functions unrelated to the visual environment (e.g. temperature regulation, detoxification and pollutant protection). Our understanding of the significance of the non‐visual functions of animal integumentary pigments, as well as how they interact with the visually related functions to shape animal visual systems, remains limited. Furthermore, due to their important roles in shaping species interactions and mediating interactions in the environment, animal colour traits are likely to be impacted by global change (e.g. increased temperatures, altered habitat quality and quantity, increased environmental stochasticity, pollutants and novel species assemblages). Considering the effects of global change on both visual and non‐visual functions is important for understanding whether the selection is acting directly on the pigment or on coloration. Since changing the trait distributions can then lead to changes in visual systems, we advocate for studies to consider all potential functions of integumentary pigments, both visual and non‐visual functions and their interaction. Towards this goal, we first highlight common functions of pigments with a focus on non‐visual functions across animal systems. Then we synthesize our current understanding of how global change can impact pigmentation and discuss factors that can modify the interactions between climate change and pigment function. Lastly, we discuss how changes in colour traits can impact visual systems and provide an example using amphibians and their responses to climate change as a model. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Changes in wildfire regimes are of growing concern and raise issues about how well we can model risks in a changing climate. Process-based coupled firevegetation models, used to project future wildfire regimes, capture many aspects of wildfire regimes poorly. However, there is now a wealth of information from empirical studies on the climate, vegetation, topography and human activity controls on wildfire regimes. The measures used to quantify these controls vary among studies, but certain variables consistently emerge as the most important: gross primary production as a measure of fuel availability, vegetation cover as a measure of fuel continuity, and atmospheric humidity as a measure of fuel drying. Contrary to popular perception, ignitions are generally not a limiting factor for wildfires. In this review, we describe how empirical fire models implement wildfire processes, synthesise current understanding of the controls on wildfire extent and severity, and suggest ways in which fire modelling could be improved. [ABSTRACT FROM AUTHOR]

The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi‐decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse‐resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream. Plain Language Summary: The Northwest Atlantic Shelf is experiencing a rapid rise in ocean temperatures, one of the fastest globally, impacting the region's marine resources. Global coupled models struggle to accurately simulate this regional warming and have large uncertainties associated with the future evolution of this warming. To address this issue, we developed a high‐resolution decadal prediction system for the Northwest Atlantic Shelf which downscales global decadal predictions using a high‐resolution regional ocean model. The downscaled simulations accurately predict past oceanic variability and forecast a temporary pause in warming over the next decade due to natural changes in the Atlantic meridional overturning circulation and the position of the Gulf Stream. This predictive capability could pave the way for the implementation of effective mitigation strategies, benefiting coastal communities and marine resources alike. Key Points: Dynamical downscaling enhances decadal prediction skill of Northwest Atlantic Shelf sea surface temperaturesA temporary respite from rapid Northwest Atlantic Shelf warming is forecast for the coming decadeThe forecast warming pause is attributed to a modest strengthening of the Atlantic meridional overturning circulation [ABSTRACT FROM AUTHOR]

Mesoscale Convective Systems (MCSs) play a critical role in tropical rainfall patterns and circulations. To reduce persistent biases and improve understanding of the climate system, international groups have called for unprecedented investment in global convection–permitting (CP) climate models. It is essential such models accurately represent MCSs, and in particular environmental interactions such as dynamical control by wind shear. We show that in representative current generation CP simulations, MCS updraft entrainment decreases with shear, leading to a realistic increase of extreme rainfall. We find the control of environmental shear extends to mean storm rainfall and anvil heights. The simulation of these effects depends strongly on model physics in both CP and parameterized models. We show that in West Africa, MCS shear response influences the zonal distribution of storm diabatic heating, modifying upscale impacts of convection. Our results demonstrate key tests for focused process–based assessment of CP model fidelity. Plain Language Summary: Movements of tropical air are driven by interactions between large, continental–scale circulations and localized deep convection — cells of ascending moist air reaching to the tropopause. These cells can aggregate to form Mesoscale Convective Systems (MCSs), long–lived thunderstorm clusters with large cold cloud shields that dominate tropical rainfall and atmospheric heating, forcing air circulations. Meanwhile, the strength of MCS rainfall depends on local atmospheric conditions. Such local process interactions occur on scales much smaller than typical global climate model grid spacings (>25 km), thus requiring a sub–grid parameterization of deep convection that introduces model biases. Here we show that kilometer–scale models can realistically capture the response of MCSs to local atmospheric moisture and wind conditions. A realistic response to the wind field relies on models showing variable mixing of dry environmental air into convective updrafts, with reduced rates for stronger vertical wind shear. We find that this wind (i.e., dynamical) control on MCSs explains patterns of mean rainfall biases in West Africa and influences atmospheric heating, likely mediating MCSs' upscale impacts on tropical circulations. Our results provide vital process–based methods for verifying the added value of explicitly representing convective storms in novel kilometer–scale global models. Key Points: Convection‐permitting models can capture the response of Mesoscale Convective Systems to both moisture and shear in West AfricaStrong shear gives larger cores, reduced entrainment and greater maximum and mean storm rainfall; this is missed in a parameterized modelShear response has upscale impacts through control of zonal distribution of biases in storm mean rainfall and diabatic heating [ABSTRACT FROM AUTHOR]

The globally averaged diurnal temperature range (DTR) has shrunk since the mid‐20th century, and climate models project further shrinking. Observations indicate a slowdown or reversal of this trend in recent decades. Here, we show that DTR has a minimum for average temperatures close to 0°C. Observed DTR shrinks strongly at colder temperature, where warming shifts the average temperature toward the DTR minimum, and expands at warmer temperature, where warming shifts the average temperature away from the DTR minimum. Most, but not all climate models reproduce the minimum DTR close to average temperatures of 0°C and a stronger DTR shrinking at colder temperature. In models that reproduce the DTR minimum, DTR shrinking slows down significantly in recent decades. Models project that the global‐mean DTR will shrink over the 21st century, and models with a DTR minimum close to 0°C project slower shrinking than other models. Plain Language Summary: The diurnal temperature range, that is, the difference between daily maximum and minimum temperatures, affects both human health and plant development. Global data sets have shown a shrinking diurnal temperature range since the 1950s, but a recent study found that the diurnal temperature range had expanded again. In this study, we investigate how the diurnal temperature range behaves at different mean temperatures. We find that the diurnal temperature range is particularly small for temperatures close to the melting point of water (0°C), and larger for both colder and warmer temperatures. Due to the latent heat of freezing/melting, more energy is required to warm the soil from −1 to +1°C than from −3 to −1°C or from 1 to 3°C. The diurnal temperature range shrinks in regions and seasons with negative average temperature, where global warming pushes the average closer to 0°C and expands for warmer temperature. Most, but not all climate models also show a narrow diurnal temperature range near 0°C, shrinking of the temperature range at colder temperature and a slower reduction in the diurnal temperature range in recent decades. Models suggest that the currently observed expansion of the diurnal temperature range is a transient phenomenon. Key Points: The diurnal temperature range (DTR) has a local minimum near average temperatures of 0°CDTR in observations shrinks strongly for average temperatures below 0°C and expands for warmer temperaturesClimate models that reproduce the local DTR minimum near 0°C show a significant slowdown of DTR shrinking in recent decades [ABSTRACT FROM AUTHOR]

Wet and cold biases on the Tibetan Plateau (TP) commonly exist in global and regional climate simulations. Previous studies have explored the possible causes of wet and cold biases and contributed to reducing these biases. However, the connection between wet and cold biases remains insufficiently addressed. Our research indicates that the TP wet bias converts into positive snow amount bias not continually but efficiently and concentratedly, under the control of snow phenology in different regions. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. Our research highlights the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, providing a novel perspective to understand the intrinsic connection between wet and cold biases and improve climate simulations on the TP. Plain Language Summary: Due to the harsh environment, observational data are limited on the Tibetan Plateau (TP), so climate model is one of necessary tools for climate researches. However, both global and regional climate models generally overestimate precipitation (wet bias) and underestimate near surface air temperature (cold bias) on the TP. Previous studies have pointed out the possible causes of the wet and cold biases, and partly reduced these biases, leaving the connection between these biases unaddressed. Our study finds that the conversion of wet to cold bias during snow season is divergent in different areas of the TP. Under the control of snow duration in different areas, the wet bias efficiently and concentratedly transforms into larger snow amount. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. We reveal the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, and these conclusions will contribute to a better improvement of snow and climate modeling on the TP. Key Points: WRF simulations significantly overestimate snow amount and delay snow phenology on the Tibetan Plateau (TP)Snowfall bias is not always transformable into snow amount bias, depending on snow phenologyComplex relationship between snow amount and surface albedo restricts the transformation of wet to cold bias on the TP [ABSTRACT FROM AUTHOR]

We propose the lag‐1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer‐scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface‐atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models. Plain Language Summary: Rainfall in the tropics is influenced by many atmospheric processes that depend on geographic location. We use the lag‐1 autocorrelation as a metric for the day‐to‐day persistence of rainfall. We find that rainfall is very persistent in most parts of the tropics with a few exceptions over land, for example, the Sahel, where high frequency rainfall events dominate. Our results show that models with a horizontal resolution of a few kilometers reproduce the autocorrelation, in contrast to coarser climate models. We also analyze atmospheric waves and find that they are important for the autocorrelation pattern in the observations and the simulations. Key Points: The lag‐1 autocorrelation pattern of daily precipitation in the tropics is robust across different observation‐based data setsThe lag‐1 autocorrelation reflects the relative variance of high frequency and low frequency convectively coupled equatorial wavesKilometer‐scale models capture the observed autocorrelation, but models with parameterized deep convection overestimate it [ABSTRACT FROM AUTHOR]

Extreme rainfall events (EREs) caused by convection are a major prediction challenge and can lead to deadly flash flooding. This study investigates EREs from 2003 to 2023 over the central and eastern United States using high‐resolution (4‐km) Stage IV precipitation analyses. EREs were defined where 12‐hr accumulations exceeded the 10‐year average recurrence interval thresholds. Summertime (June–August) EREs exhibited pronounced interannual variability, but with statistically significant increasing trends in annual frequency, particularly among those caused by mesoscale convective systems. Significant relationships were also found between summertime convective EREs over the central United States and enhanced poleward moisture transport, consistent with a westward expansion of the climatological North Atlantic Subtropical High. This relationship may aid in medium‐ and long‐range forecasting. However, the spatial scale of EREs often included localized or narrow bands of extreme accumulations, revealing significant challenges in the accurate representation of EREs in weather and climate models. Plain Language Summary: Extreme rainfall events often result from slow‐moving thunderstorms that drop heavy rain over the same area for several hours, which can cause deadly flash flooding. This study defines an extreme rainfall event where the 12‐hr accumulation would only exceed that amount once every 10 years on average, with a focus on the summer months over the central and eastern United States from 2003 to 2023. Our results show a significant increasing trend in the number of extreme events, especially those caused by larger, longer‐duration storms. Our use of a high‐resolution rainfall data set also revealed that extreme rainfall often occurs due to nighttime thunderstorms producing heavy rainfall over highly localized areas or narrow (20–40 km wide) swaths. These characteristics present a challenge for global weather and climate models. The frequency of these summertime extreme events also significantly varied from year to year. Summers with more extreme events were associated with a greater northward transport of moist air around the western edge of an area of high pressure that extends westward from the Atlantic Ocean. The relationship between this flow pattern and extreme rainfall suggests a possible path for improving forecasts of extreme rainfall. Key Points: High‐resolution rainfall data reveal an increasing trend in extreme summertime rainfall, especially from mesoscale convective systemsMore events occurred during summers with greater southerly moisture transport driven by differences in the North Atlantic Subtropical HighThe relatively small scale of extreme convective rainfall presents a challenge for representing these events in weather and climate models [ABSTRACT FROM AUTHOR]

The physical processes governing a tropical cyclone's lifecycle are largely understood, but key processes occur at scales below those resolved by global climate models. Increased resolution may help simulate realistic tropical cyclone intensification. We examined fully coupled, global storm‐resolving models run at resolutions in the range 28–2.8 km in the atmosphere and 28–5 km in the ocean. Simulated tropical cyclone activity, peak intensity, intensification rate, and horizontal wind structure are all more realistic at a resolution of ∼5 km compared with coarser resolutions. Rapid intensification, which is absent at typical climate model resolutions, is also captured, and exhibits sensitivity to how, and if, deep convection is parameterized. Additionally, the observed decrease in inner‐core horizontal size with increasing intensification rate is captured at storm‐resolving resolution. These findings highlight the importance of global storm‐resolving models for quantifying risk and understanding the role of intense tropical cyclones in the climate system. Plain Language Summary: Simulating strong tropical storms (i.e., major hurricanes, super typhoons) with climate models is challenging because important processes that act to intensify a storm occur over spatial scales that are too small for global models to capture. Typical models lack sufficient resolution in the atmosphere and ocean, often constrained by computational resources. Recently, in a few models, resolution has increased to a point where each grid cell represents an area of just a few square kilometres, a significant leap of one or two orders of magnitude. We analyzed tropical storms simulated by these state‐of‐the‐art, so‐called storm‐resolving models and found that peak tropical storm intensity and the rate at which storms intensify are both more realistic. These models also simulate the rapid intensification of tropical storms and capture the small eye diameters often seen in the most intense storms. Our work provides evidence that storm‐resolving resolution may help us better understand the role of tropical storms in the climate system and predict their behavior in a warming climate. Key Points: Simulated tropical cyclone characteristics analyzed in two fully coupled global climate models at atmospheric resolutions of 28 to 2.8 kmTropical cyclone intensification rate is close to observations at resolutions of 5 km or finer, and rapid intensification is capturedStorm‐resolving models also capture the observed relationship between high intensification rate and small inner‐core size [ABSTRACT FROM AUTHOR]

We simulate the geometric albedo spectra of hot Jupiter exoplanets HD 209458 b and WASP-43 b, based on global climate model post-processed with kinetic cloud models. We predict WASP-43 b to be cloudy throughout its dayside, while HD 209458 b has a clear upper atmosphere around the hot subsolar point, largely due to the inclusion of strong optical absorbers TiO and VO in the general circulation model for the latter causes a temperature inversion. In both cases our models find low-geometric albedos – 0.026 for WASP-43 b and 0.028 for HD 209458 b when averaged over the CHaracterizing ExOPlanet Satellite bandpass of |$\sim$| 0.35–1.1  μ m – indicating dark daysides, similar to the low albedos measured by observations. We demonstrate the strong impact of clouds that contain Fe-bearing species on the modelled geometric albedos; without Fe-bearing species forming in the clouds, the albedos of both planets would be much higher (0.518 for WASP-43 b, 1.37 for HD 209458 b). We conclude that a cloudy upper or mid-to-lower atmosphere that contains strongly absorbing Fe-bearing aerosol species, is an alternative to a cloud-free atmosphere in explaining the low-dayside albedos of hot Jupiter atmospheres such as HD 209458 b and WASP-43 b. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change and associated increasing nutrient loading to coasts will worsen coastal eutrophication on a global scale. Basin Head is a coastal lagoon located in northeastern Prince Edward Island, Canada, with a federally protected ecosystem. Nitrate-nitrogen (NO3-N) is conveyed from agricultural fields in the watershed to the eutrophic lagoon via intertidal groundwater springs and groundwater-dominated tributaries. A field program focused on four main tributaries that discharge into the lagoon was conducted to measure year-round NO3-N loading. These measurements were used to calibrate a SWAT+ hydrologic model capable of simulating hydrologic and NO3-N loads to the lagoon. Several climate change scenarios incorporating different agricultural best management practices (BMPs) were simulated to better understand potential future NO3-N loading dynamics. Results indicate that all climate change scenarios produced increased annual NO3-N loading to the lagoon when comparing historical (1990-2020) to end of century time periods (2070-2100); however, only one climate scenario (MRI-ESM2-0 SSP5-8.5) resulted in a statistically significant (p-value <0.05) increase. Enlarged buffer strips and delayed tillage BMP simulations produced small (0%-8%) effects on loading, while changing the crop rotation from potato-barley-clover to potato-soybean-barley yielded a small reduction in NO3-N loading between the historical period and the end of the century (26%-33%). Modeling revealed changes in seasonal loading dynamics under climate change where NO3-N loads remained more consistent throughout the year as opposed to current conditions where the dominant load is in the spring. An increase in baseflow contributions to streamflow was also noted under climate change, with the largest change occurring in the winter (e.g., up to a five-fold increase in February). These findings have direct implications for coastal management in groundwater-dominated agricultural watersheds in a changing climate. [ABSTRACT FROM AUTHOR]

Legumes are essential plants in dryland ecosystems worldwide because they increase nitrogen availability, so their understanding is vital for improving knowledge and modelling in the face of climate change. This work studies the differences in resource use efficiency and their relationship with photosynthetic, photochemical, bioelemental, and stoichiometric traits of coexistent legumes and non‐legumes in a Sonoran Desert ecosystem. We found that legumes had higher photosynthetic rates, intrinsic and seasonal water use efficiency (WUE), phosphorus use efficiency (PPUE), and higher light utilisation mediated by chlorophyll content and active reaction centers, which may increase their photoprotection. Legumes can increase their WUE and PPUE with no changes in nitrogen use efficiency (PNUE). Consequently, observed trait relationships between studied traits in these legumes have significant differences with the non‐legume species in the study. Stoichiometry is helpful, in some cases, as an indicator of nutrient use efficiency and enables functional group differentiation. Our results strongly relate legumes' higher resource use efficiency with their success in dryland ecosystems. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Taxon Methods Results Main Conclusions We aimed to assess potential climate change impacts on the distribution of a cold‐adapted terrestrial amphibian that shows strong intraspecific differentiation.Alps and Dinarides.Alpine salamander (Salamandra atra).We built SDMs for each of two major intraspecific lineages (S. a. atra, which is found throughout the Northern Alps, and S. a. prenjensis, which appears sparsely along the Dinarides) and for the entire species, using occurrence points from a carefully curated database, climate data with 1 km2 spatial resolution and eight modelling techniques. We projected climatically suitable areas to 2070 under two climate scenarios (RCP2.6 and RCP8.5), using 24 general circulation models.For S. a. atra, under RCP2.6 scenario, we detected a 3% increase in the extent of climatically suitable areas in the Alps (although with low model agreement), yet a 6% decrease under the RCP8.5 scenario. For S. a. prenjensis, we detected a decrease in the extent of climatically suitable areas in the Dinarides between 7% and 45% (RCP2.6 and RCP8.5, respectively). The SDM predictions for the entire species were most in line with those of the S. a. atra model. Although climate variables were not equally strong in predicting the climatic suitability for both lineages, the minimum temperature of the coldest month revealed an important predictor for the entire species, with clear decreases in suitability towards higher temperature.All models reveal a considerable decrease in climate suitability throughout most of the range of S. atra, which is particularly visible in the Dinarides. We highlight the importance of considering intraspecific variation when modelling climate change impacts on geographically differentiated species whose populations vary in their climatic niche. We provide our study results (occurrence data and maps) via a web application that can be useful for guiding conservation efforts. [ABSTRACT FROM AUTHOR]

Drought is one of the most devastating threats to the livelihoods of the southern African population, who mainly rely on rain‐fed agriculture for income. Previous studies have highlighted that the Botswana High influences drought over the region; however, its influence on the spatial modes of drought remains unknown. This study examines the spatiotemporal structures of drought modes (DMs) over southern Africa and their link with the Botswana High in observation, reanalysis and Model for Prediction Across Scales (MPAS). To characterize droughts, the study uses the 3‐month scale standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). Spatiotemporal characteristics of the DMs are identified using empirical orthogonal function (EOF) analysis on SPI and SPEI. EOF analysis is also used to identify the spatiotemporal characteristics of the Botswana High. The relationship between each DM and the Botswana High is quantified using correlation and R2 analysis. In all the datasets (Climate Research Unit (CRU), European Centre for Medium‐Range Weather Forecasts version 5 (ERA5), 20th Century reanalysis II (20C) and MPAS), the most dominant five DMs (hereafter DM1–DM5) over southern Africa jointly explain more than 60% of the interannual variability in the 3‐month scale summer droughts for SPEI and SPI. CRU, ERA5 and MPAS agree that the Botswana High correlates with the interannual variability of DM1, with a stronger correlation in ERA5 (r = −0.85) compared to MPAS (r = −0.42) and CRU (r = −0.35). Additionally, wet years (+ve SPEI and SPI) are characterized by a weak Botswana High and drought years (−ve SPEI and SPI) by a strong Botswana High. The wet and dry years correspond to the −ve and +ve phases of El Niño–Southern Oscillation (ENSO), respectively. Given this, the results of this study suggest that the Botswana High might be a teleconnection pattern through which ENSO signals influence DM1 over the region. [ABSTRACT FROM AUTHOR]

Dams commonly restrict fish movements in large rivers but can also help curtail the spread of invasive species, such as invasive bigheaded carps (Hypophthalmichthys spp). To determine how dams in the upper Mississippi River (UMR) affect large-scale invasive and native fish migrations, we tracked American paddlefish (Polyodon spathula) and bigheaded carp across > 600 river km (rkm) and 16 navigation locks and dams (LD) of the UMR during 2 years with contrasting water levels. In 2022, a low-water year, both native paddlefish and invasive bigheaded carp had low passage rates (4% and 0.6% respectively) through LD15, a movement bottleneck being studied for invasive carp control. In contrast, flooding in 2023 led to open-river conditions across multiple dams simultaneously, allowing 53% of paddlefish and 46% of bigheaded carp detected in Pool 16 to move upstream through LD15. Bigheaded carp passed upstream through LD15 rapidly (μ = 32 rkm per day) a maximum of 381 rkm, whereas paddlefish moved an average of 9 upstream rkm per day (maximum of 337 rkm). Our results can inform managers examining trade-offs between actions that enhance native fish passage or deter movements of invasive species. This understanding is critical because current climate change models project increases in flooding events like that observed during 2023. [ABSTRACT FROM AUTHOR]

A primary driver of deep-ocean mixing is breaking of internal tides generated via interactions of barotropic tides with topography. It is important to understand how the energy conversion from barotropic to internal tides responds to global warming. Here we address this question by applying a linear model of internal tide generation to coupled global climate model simulations under a high carbon emission scenario. The energy conversion to high-mode internal tides is projected to rise by about 8% by the end of the 21st century, whereas the energy conversion to low-mode internal tides remains nearly unchanged. The intensified near-bottom stratification under global warming increases energy conversion into both low and high-mode internal tides. In contrast, the intensified depth-averaged stratification reduces the modal horizontal wavenumber of internal tides, leading to increased (decreased) energy conversion into high (low)- mode internal tides. Our findings imply stronger mixing over rough topography under global warming, which should be properly parameterized in climate models for more accurate projections of future climate changes. The authors use a linear model of internal tide generation and an ensemble of coupled global climate model simulations under a high emission scenario, and find a significant enhancement of energy conversion to high-mode internal tides by the end of the 21st century. [ABSTRACT FROM AUTHOR]

The shifting thermal environment brought by global warming presents new concerns for urban residents. However, the lack of urban presentation in the global and regional climate models limits the ability of these models to provide useful information at the urban scale. This study examines the impact of 1.5 °C and 2 °C global warming levels (GWL1.5 and GWL2) on the future summer of Beijing, China. A new statistical-dynamical downscaling (SDD) method was applied using available Coordinated Regional Climate Downscaling Experiment (CORDEX) ensemble data to downscale climate projections across Beijing at different GWLs. The results showed that the maximum air temperature increase reached 3.5 °C and 4 °C at GWL1.5 and GWL2, respectively, in the central urban area of Beijing. The historical urban heat island (UHI) intensity first increased to 2.48 ± 0.97/1.02 ± 0.58 °C in GWL1.5 and then decreased to 2.24 ± 0.98/0.90 ± 0.69 °C in GWL2 at 22:00/09:00. Under GWL1.5, the UHI effect is greater in the eastern metropolitan areas (> 2 °C) than in the western regions (0.5–1.5 °C). The highest daytime and nighttime UHIs occurred mostly in LCZ154 (open high-rise area). The intensity, duration, and frequency of future heat waves (HWs) are increasing, especially in urban areas under GWL2. Climate information on UHIs and HWs under the Paris Agreement would be very helpful for stakeholders and city planners to develop near-term future local adaptation policies. [ABSTRACT FROM AUTHOR]

The seasonal prediction of the Indian summer monsoon (ISM) and Monsoon Intraseasonal Oscillations (MISO), as well as the Madden Julian Oscillations (MJO) that strongly modulate MISO, is important to the country for water and crop management. We have analyzed the precipitation, convection, and cloud in the selected models from the sixth generation Coupled Model Intercomparison Projects (CMIP6). This study highlights the significant differences in simulating MISO and MJO features between the selected CMIP6 models and their possible reasons. The mean and intraseasonal features of MISO and MJO varied significantly in CMIP6 models, which may impact better depiction of convection and total cloud fraction. The probability distributions of rainfall and OLR in CMIP6 models also indicate significant variations in simulating the MISO and mean ISM. The results demonstrate the importance of cloud and convection in CMIP6 models to depict realistic MISO and MJO and provide a road map for improving ISM climate prediction and projections. [ABSTRACT FROM AUTHOR]

The sustainable management of forestry activities, together with changes in vegetation due to deforestation or degradation, contributes to sediment risk and increases the risk of surface runoff. Changes in meteorological criteria, such as precipitation and temperature, as a result of global climate change are also significant factors affecting sediment risk. In this study, sediment risk was predicted spatially and temporally for 65 provinces in Turkey using criteria related to average forest road construction rates and average wood harvesting rates for the period between 2017 and 2021, as well as climate change models (GFDL-ESM2M, HadGEM2-ES, and MPI-ESM-MR) and their scenarios (RCP 4.5 and RCP 8.5) for five-year periods between 2022 and 2096. In addition, changes in sediment risk in the short and long terms—that is, trends—were determined in spatially and temporally. Entropy-based WASPAS and fuzzy clustering analysis were used together to determine sediment risk in this context. The results show that, in terms of sediment risk, criteria related to forestry activities had a higher weight than criteria related to climate change when looking at the overall criterion weights. In addition, it was generally observed that the contribution of the average precipitation criterion to sediment risk increased in weight over five-year periods in the context of climate change models and scenarios. Regarding climate change models and scenarios, it was found that provinces consistently in the highest risk category (R1) over five-year periods were mainly located in the Black Sea and Marmara regions. In addition, provinces showing an increase or decrease in sediment risk trends between two consecutive five-year periods were mostly found in the Black Sea and Mediterranean regions. When evaluating the 15-year time intervals, differences in sediment risk trends were found between the geographical regions. In conclusion, the study results indicate that, regionally, Turkey's northern regions, especially the Black Sea and Marmara regions, as well as the southern Mediterranean and western Aegean regions, will become increasingly vulnerable to sediment risk over time owing to the impact of climate change. [ABSTRACT FROM AUTHOR]

Surface Air Temperature (SAT) predictions, typically generated by Global Climate Models (GCMs), carry uncertainties, particularly across different greenhouse gas emission scenarios. Machine Learning (ML) techniques can be employed to forecast long-term temperature variations, although this is a challenging endeavour with few drawbacks, such as the influence of scenarios involving greenhouse gas emissions. Therefore, the present study utilized multiple ML approaches such as Artificial Neural Networks (ANN), multiple linear regression, support vector machine and random forest, along with various daily predicted results of GCMs from Coupled Model Intercomparison Project Phase 6 as predictors and the "India Meteorological Department's" Maximum SAT (MSAT) as the predictand, to predict daily MSAT in the months of March, April and May (MAM) over Andhra Pradesh (AP) for the period 1981–2022. The results show that ANN outperforms other ML techniques in predicting daily MSAT, with a root mean square error of 1.41, an index of agreement of 0.89 and a correlation coefficient of 0.81. The spatial distribution of hot and heat wave days indicates that the Multiple Model Mean (MMM) underestimates these occurrences, with a minimum bias of 9 and 6 days, respectively. In contrast, the ANN model exhibits much smaller biases, with a maximum underestimation of 3 hot and 2 heat wave days. These findings demonstrate that MMM does not capture the maximum temperatures well, resulting in poor predictability. Further, future temperature projections were analysed from 2023 to 2050, which display a gradual increase in mean MSAT during MAM over AP. This research demonstrates the potential of ML techniques to enhance temperature forecasting accuracy, offering valuable insights for climate modeling and adaptation. The results are crucial for stakeholders in agriculture, health, energy, water resources, socio-economic planning, and urban development, aiding in informed decision-making and improving resilience to climate change impacts. [ABSTRACT FROM AUTHOR]

Climate models enable an analysis of observation data related to global warming and climate changes, and they allow making predictions for near the future progress of climate changes. In this study, basics of the climate model, which is used in the analysis of global warming and climate change topics, and the analyses that can be carried out by using these models, are reviewed. Simplified energy balance model considers the balance state between the incoming solar energy from the sun and the radiation energy outgoing from the earth to space. In this perspective, the model allows considering the factors, which can influence global warming, as radiative forces that are acting on the balance state. Thus enabling simplified analyzes of climate changes based on the energy budget even though the climate changes depend on very complex and dynamically changing meteorological and geological factors. In this study, within the framework of basic radiation laws, such as Stefan-Boltzmann and Wien Displacement, the absorption and reflection rates of the solar radiation by the earth, and the atmospheric window concepts, which provide energy escape from the earth's atmosphere to the space within the long-wavelength of the electromagnetic spectrum, were reviewed. In this context, the radiative forcing due to the Albedo effect and the radiative forcing effects due to the accumulation of CO2 molecules in the atmosphere, which have the potential to alter depending on human activities, were explained. Illustrative examples and observation data-based calculations and analyzes are presented to make basic concepts and presented models easy-to-understand. This article is intended to be a tutorial that explains some basic concepts on the topics of global warming and climate change. [ABSTRACT FROM AUTHOR]

The CICERO Simple Climate Model (CICERO-SCM) is a lightweight, semi-empirical model of global climate. Here we present a new open-source Python port of the model for use in climate assessment and research. The new version of CICERO-SCM has the same scientific logic and functionality as the original Fortran version, but it is considerably more flexible and also open-source via GitHub. We describe the basic structure and improvements compared to the previous Fortran version, together with technical descriptions of the global thermal dynamics and carbon cycle components and the emission module, before presenting a range of standard figures demonstrating its application. A new parameter calibration tool is demonstrated to make an example calibrated parameter set to span and fit a simple target specification. CICERO-SCM is fully open-source and available through GitHub (https://github.com/ciceroOslo/ciceroscm , last access: 23 August 2024). [ABSTRACT FROM AUTHOR]

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost. [ABSTRACT FROM AUTHOR]

Changes in leaf temperature are known to drive stomatal responses, because the leaf‐to‐air water vapour gradient (Δw) increases with temperature if ambient vapour pressure is held constant, and stomata respond to changes in Δw. However, the direct response of stomata to temperature (DRST; the response when Δw is held constant by adjusting ambient humidity) has been examined far less extensively. Though the meagre available data suggest the response is usually positive, results differ widely and defy broad generalisation. As a result, little is known about the DRST. This review discusses the current state of knowledge about the DRST, including numerous hypothesised biophysical mechanisms, potential implications of the response for plant adaptation, and possible impacts of the DRST on plant‐atmosphere carbon and water exchange in a changing climate. Summary statement: The direct response of stomata to temperature (the response when evaporative demand is held constant by adjusting ambient humidity) is very poorly known. This review summarises the current state of knowledge about the response, a range of potential biophysical mechanisms, and its possible implications for plant‐atmosphere gas exchange and evolution. [ABSTRACT FROM AUTHOR]

In recent years, the frequency of floods has escalated due to global warming and human activities. Addressing this challenge, our study investigates how future climate change scenarios will affect flood susceptibility in the Tajan watershed, northern Iran. The primary objective is to quantify and map the evolving risk of flooding in this region under different future climate scenarios. We applied machine learning techniques, coupled model intercomparison project phase 6 (CMIP6) climatic models, and remote sensing to achieve this goal. The CanESM5 climate model was chosen for its accuracy among four global climate models in CMIP6 to estimate future precipitation trends under shared socioeconomic pathways (SSP 2.6, 4.5, and 8.5) over two-time horizons: future (2020–2060) and far future (2061–2100). These scenarios encompass various influential factors, such as greenhouse gas emissions, urbanization, deforestation, and socioeconomic development, which played crucial roles in modulating flood susceptibility. Flood susceptibility maps were generated considering future precipitation patterns and scenarios using random forest (RF) and support vector machine (SVM) algorithms, 432 flood locations, and 15 flood influencing factors. The accuracy of our prediction was validated through multiple statistical measures, including the area under the receiver operating characteristic (AUC-ROC) curve. The results indicated that the proposed models performed well, with the RF model (AUC=0.91) demonstrating higher accuracy compared to the SVM model (AUC=0.85). From a spatial perspective, increased future precipitation under all SSP scenarios enhances the likelihood of flood occurrences in the central and downstream regions. In the far future, intensified precipitation due to changes in regional topography and climate, coupled with higher greenhouse gas concentrations, is expected to heighten flood risks, especially at higher altitudes. We hope that our study findings will inform effective flood risk management strategies and adaptation plans in response to climate-induced flood risks, both in our study area and in similar regions globally. [ABSTRACT FROM AUTHOR]

Climate change is associated with increased mean temperatures and amplitudes manifesting both acutely and chronically, triggering organism stress responses that confer fitness costs and/or benefits. The larger grain borer (LGB), Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) is an invasive postharvest agricultural pest. While host shift is its well‐known potential invasive mechanism, how repeated intergenerational stress environments may influence offspring phenotypes is largely unknown. We thus evaluated physiological and ecological performance of LGB following repeated intergenerational acute heat acclimation to insinuate its likely responses to projected increased bouts of heat stress associated with climate change. Parental colonies were acutely heat‐acclimated separately at 35°C and 38°C; 80% RH for 2 h in climate chambers and released onto sterilized maize grain at optimal conditions (32°C, 80% RH). The F1 progenies were, respectively, acclimated at the same conditions and incubated to F2 generation. We then evaluated physiological and ecological performance under optimal conditions across parental, F1 and F2 generations. Our results showed that plasticity was highly trait dependent, and that acclimation did not affect F1 and F2 critical thermal maxima, but did improve critical thermal minima. However, while acclimation improved heat knockdown time at F1, repeated acclimation significantly reduced heat knockdown times at F2, suggesting plasticity erosion with generational repeated acclimations. Acute acclimation negatively affected ecological performance of F1 generations although this was restored with repeated acclimation in F2 populations. Our results suggest that the LGB may inflict more economic damage with repeated heat stress due to generational adaptation to temperature stress. The results contribute to knowledge on pest forecasting modelling under changing climates and provides a framework for phytosanitary adjustments in heat treatment protocols for international grain trade. [ABSTRACT FROM AUTHOR]

Quantum approaches to International Relations (IR) offer theoretically rich explanatory frameworks attuned to the complexity and uncertainty of the social world. Recognizing that the payoff of quantum approaches to IR may be clarified through their application to empirical cases, we approach the radically complex and uncertain case of climate change's impacts on Community-Based Natural Resource Management (CBNRM) in Namibia from a quantum perspective. Established to protect the vibrant flora and fauna of Namibia while also promoting community and economic development aims, CBNRM conservancies face complex challenges from climate change. Inspired by Karen O'Brien's call for 'quantum social change' in our response to climate change, we draw on the quantum social theory to unpack how desertification, extreme weather patterns, and drought conditions radically reshape the possibilities available to conservancies, communities, farmers, and the state itself. By conceptualizing futures as wavefunctions encompassing the spectrum of potential future states, we demonstrate how a quantum imaginary can help to develop novel explanatory frameworks for the complexity of the world around us. [ABSTRACT FROM AUTHOR]

Sudden stratospheric warmings (SSWs) are dramatic events in the polar winter stratosphere that are accompanied by atmospheric parameter anomalies in the stratosphere and mesosphere. Microwave Limb Sounder and Global Navigation Satellite System Occultation Sounder observations on board the Chinese FengYun 3 satellites indicate a rapid increase of over 50 % in the mesospheric density at high latitudes around the onset date during the 2021 major SSW event. The amplification of the zonal mean density around the onset is proportional to the latitude increase with a maximum increment of 83.3 % at 59 km above 80° N, which is more than 3 times larger than the climatological standard deviation (23.1 %). The horizontal density distributions are influenced by the changing polar vortex fields. A simulation using a specified dynamics version of the Whole Atmosphere Community Climate Model reproduces the global circulation and presents a severe change in the planetary wave forcing and residual meridional circulation mass flux followed by a change in the density tendency. These results demonstrate that the observed enhanced density is primarily attributed to the altered planetary waves and residual circulation during the SSW event. The observations and simulations also indicate that the density anomalies could extend to middle latitudes. Obvious density disturbances in the upper stratosphere and mesosphere were observed by the lidar deployed in Beijing (40.3° N, 116.2° E). [ABSTRACT FROM AUTHOR]

Eco-evolutionary modelling involves the coupling of ecological equations to evolutionary ones. The interaction between ecological dynamics and evolutionary processes is essential to simulating evolutionary branching, a precursor to speciation. The creation and maintenance of biodiversity in models depends upon their ability to capture the dynamics of evolutionary branching. Understanding these systems requires low-dimension models that are amenable to analysis. The rapid reproduction rates of marine plankton ecosystems and their importance in determining the fluxes of climatically important gases between the ocean and atmosphere suggest that the next generation of global climate models needs to incorporate eco-evolutionary models in the ocean. This requires simple population-level models, that can represent such eco-evolutionary processes with orders of magnitude fewer equations than models that follow the dynamics of individual phenotypes. We present a general framework for developing eco-evolutionary models and consider its general properties. This framework defines a fitness function and assumes a beta distribution of phenotype abundances within each population. It simulates the change in total population size, the mean trait value, and the trait differentiation, from which the variance of trait values in the population may be calculated. We test the efficacy of the eco-evolutionary modelling framework by comparing the dynamics of evolutionary branching in a six-equation eco-evolutionary model that has evolutionary branching, with that of an equivalent one-hundred equation model that simulates the dynamics of every phenotype in the population. The latter model does not involve a population fitness function, nor does it assume a distribution of phenotype abundance across trait values. The eco-evolutionary population model and the phenotype model produce similar evolutionary branching, both qualitatively and quantitatively, in both symmetric and asymmetric fitness landscapes. In order to better understand the six-equation model, we develop a heuristic three-equation eco-evolutionary model. We use the density-independent mortality parameter as a convenient bifurcation parameter, so that differences in evolutionary branching dynamics in symmetric and asymmetric fitness landscapes may be investigated. This model shows that evolutionary branching of a stable population is flagged by a zero in the local trait curvature; the trait curvature then changes sign from negative to positive and back to negative, along the solution. It suggests that evolutionary branching points may be generated differently, with different dynamical properties, depending upon, in this case, the symmetry of the system. It also suggests that a changing environment, that may change attributes such as mortality, could have profound effects on an ecosystem's ability to adapt. Our results suggest that the properties of the three-dimensional model can provide useful insights into the properties of the higher-dimension models. In particular, the bifurcation properties of the simple model predict the processes by which the more complicated models produce evolutionary branching points. The corresponding bifurcation properties of the phenotype and population models, evident in the dynamics of the phenotype distributions they predict, suggest that our eco-evolutionary modelling framework captures the essential properties that underlie the evolution of phenotypes in populations. [ABSTRACT FROM AUTHOR]

Treeline is a global ecological phenomenon in which tree populations decline, often abruptly, above a specific elevation or latitude. Temperature is thought to be a key determinant of treeline because it affects the rates at which trees establish, grow, produce seeds, and die. As climate change causes temperature increases, treelines have been observed to move in response—but there is considerable variability. In this study, we present a general mathematical model that provides possible explanations for both the general patterns observed in treelines and some of the variation. Avoiding system-specific details, our model assumes simply that all life processes are temperature-dependent. We incorporate the possibility of positive or negative feedback, in which the presence of trees either increases or decreases the temperature at their location. Our results indicate that this feedback and the relationship between temperature thresholds for growth, seed production, and seedling establishment are the key determinants of tree line form and movement. The model also shows that under many conditions bistability is predicted: treeline can equilibrate at two different elevations under the same conditions, depending on the system's history. General, flexible models like ours are essential for generating a unifying theory of treeline form and dynamics across multiple ecosystems. [ABSTRACT FROM AUTHOR]

Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future. [ABSTRACT FROM AUTHOR]

Estimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures. [ABSTRACT FROM AUTHOR]

Snowdrifts on lake ice provide vital breeding habitats for the endangered Saimaa ringed seal. In this study, a lake ice model of Watershed Simulation and Forecasting System (WSFS-Ice) was developed for improved estimation of ice and snow conditions in Lake Saimaa during the pupping season of the Saimaa ringed seal. The WSFS-Ice model is based on energy balance, enabling reliable estimation of the ice cover evolution in current and future climate. In addition, a simple snowdrift model was used to simulate formation of snowdrifts, which are essential for the seals breeding success in Lake Saimaa. The model was calibrated against ice thickness, ice type and snow depth measurements. According to our results based on climate scenarios with intermediate representative concentration pathway (RCP4.5), the breeding habitat of the Saimaa ringed seal is significantly deteriorating during the twenty-first century. The mean depth of the snowdrifts is projected to decrease approximately to half from the 1981–2010 to 2070–99 period and at the same time the ice-covered period is reduced by one and a half months. During the mildest winters the ice cover is projected to melt even before the pupping season has ended. The results highlight the importance of climate change mitigation and active conservation measures to enhance seal population growth, enabling it to survive in a changing climate. [ABSTRACT FROM AUTHOR]

The Paris Agreement stands as a landmark in global economies' commitment to accelerate their sustainable transition and promote food security. Efforts to mitigate and adapt to climate change are high on the agenda of both policymakers and researchers who increasingly acknowledge the criticality of innovative measures to support those efforts. However, it is still unclear how climate change factors and innovative capabilities affect food production in the long run, especially for developing countries, which are most threatened by climate change. Using an autoregressive distributed lag (ARDL) cointegration approach, this study evaluates the long-run effects of climate change and innovative capabilities on food production in Algeria over the period 1970–2019. To do so, food production is posited as a function of a set of climate change variables (CO2 emissions, mean temperatures, and mean precipitations) and innovative capabilities (human and physical capital). The novelty of this approach helps to tease out the distinct effects of these factors on food production, both in the long run and the short run. The results not only support the presence of long-run relationships between the variables but also show that, while precipitations, human capital, and physical capital positively affect food production, temperatures have a negative relationship with food production. Moreover, CO2 emissions do not appear to have a long-run effect on food production, at least not directly. In the short run, results confirm that food production is positively related to precipitation and stock of physical capital. Results from this study thus suggest that mitigating climate change impacts and ensuring food security, especially in developing countries, will require investments in agricultural innovation. [ABSTRACT FROM AUTHOR]

This paper highlights how technology can contribute to reaching the 2015 Paris Agreement goals of net zero carbon dioxide (CO2) emissions and global warming below 2°C in 2100. It uses the Advanced Climate Change Long-term model (ACCL), particularly adapted to quantify the consequences of energy price and technology shocks on CO2 emissions, temperature, climate damage and Gross Domestic Product (GDP). The simulations show that without climate policies the warming may be +5°C in 2100, with considerable climate damage. An acceleration in 'usual' technical progress not targeted at reducing CO2- even worsens global warming and climate damage. According to our estimates, the world does not achieve climate goals in 2100 without 'green' technologies. Intervening only via energy prices, e.g. a carbon tax, requires challenging hypotheses of international coordination and price increase for polluting energies. We assess a multi-lever climate strategy combining energy efficiency gains, carbon sequestration, and a decrease of 3% per year in the relative price of 'clean' electricity with a 1 to 1.5% annual rise in the relative price of polluting energy sources. None of these components alone is sufficient to reach climate objectives. Our last and most important finding is that our composite scenario achieves the climate goals. [ABSTRACT FROM AUTHOR]

To separate the contributions of climate change and human activities to alterations in flow for the Ahar Chay watershed in Iran, change points of the normalized difference vegetation index (NDVI) and evapotranspiration (ET) were distinguished before and after significant alterations. The Budyko model and Soil and Water Assessment Tool (SWAT) were used to find the reduction in runoff. New Penman-Monteith-Leuning (PML) ET data was used to consider plant types. Results revealed an increase in NDVI coinciding with the change point in 1998, as well as ET in 1999. The significance of the Sattarkhan Dam became evident when distinguishing the impacts upstream versus downstream of the dam. Results also indicated that climate change contributed significantly to upstream flow reduction (83.5%), whereas downstream reductions were primarily linked to human-related impacts (59.7–82.5%). Assuming no dam interception, 2007 became the change point, signifying a 37.5% reduction in average flow from before to after this point due to the increased vegetation cover. [ABSTRACT FROM AUTHOR]

The last deglaciation experienced the retreat of massive ice sheets and a transition from the cold Last Glacial Maximum to the warmer Holocene. Key simulation challenges for this period include the timing and extent of ice sheet decay and meltwater input into the oceans. Here, major uncertainties and forcing factors for the last deglaciation are evaluated. Two sets of transient simulations are performed based on the novel ice‐sheet reconstruction PaleoMist and the more established GLAC1D. The simulations reveal that the proximity of the Atlantic meridional overturning circulation (AMOC) to a bifurcation point, where it can switch between on‐ and off‐modes, is primarily determined by the interplay of greenhouse gas concentrations, orbital forcing and freshwater forcing. The PaleoMist simulation qualitatively replicates the Bølling‐Allerød (BA)/Younger Dryas (YD) sequence: a warming in Greenland and Antarctica during the BA, followed by a cooling northern North Atlantic and an Antarctic warming during the YD. Plain Language Summary: The last deglaciation, spanning roughly 20,000 to 10,000 years ago, marked a period of Earth's history characterized by the retreat of massive ice sheets that had covered large parts of the planet. During this phase, a drastic transition occurred from the cold Last Glacial Maximum to the warmer and more stable climate of the Holocene. A main challenge for simulating the last deglaciation is the timing and amplitude of the ice sheet decay and the amount of meltwater that enters into the oceans. Using two different reconstructions of ice sheets, we employ an efficient climate model to explore changes at the end of the last ice age. Our comparison shows notable differences in the timing and amplitude of abrupt climate events in the simulations using two different ice‐sheet reconstructions. Furthermore, we investigate the effects of factors such as greenhouse gases and Earth's orbital changes on the large‐scale ocean currents with respect to underlying ice sheets. Ultimately, our study sheds light on how different elements of the Earth's system shape the termination of the last ice age, enriching our understanding of Earth's climate history and guiding further deglaciation scenarios. Key Points: Transient simulations of the last deglaciation using GLAC1D and the new PaleoMist ice sheet reconstruction are compared for the first timeAMOC stability is impacted by ice sheet reconstruction used and the combination of forcings, indicating its proximity to a bifurcation pointPaleoMist simulation captures the BA/YD sequence signature in the northern North Atlantic [ABSTRACT FROM AUTHOR]

Brown carbon (BrC) in China is of great interest to the regional and global climate due to its strong absorption of sunlight. However, the contribution of BrC to total carbonaceous aerosol light absorption and its direct radiative effects (DRE) in China remains largely uncertain. To better assess its climate impact in China, we develop an explicit BrC scheme and implement it in a global climate model, which includes optical parameters of primary BrC derived from local measurements, secondary BrC absorption, and a photobleaching parameterization of BrC. By comparing with multi‐type observational data, we find that with the implementation of this scheme, the model captures the seasonal variations of BrC light absorption well in China. The model estimates that BrC contributes 19% and 12% to the total light absorption of carbonaceous aerosol in China in winter and summer, resulting in 0.110 and 0.205 W m−2 of DRE, respectively. Plain Language Summary: While the existing research on the direct radiative effects (DRE) of brown carbon (BrC) predominantly focuses on biomass‐burning sources, little attention has been given to BrC originating from anthropogenic sources. Coal combustion and residential fuels used for cooking and heating release many brown carbon aerosols in China. Source of BrC in China is different from that in Europe and North America. Therefore, studying the light‐absorbing properties and climate effects of BrC in China is of great scientific significance. Here, we introduce local optical parameters of primary BrC based on observations in China, secondary BrC absorption, and chemical bleaching of BrC in a global climate model (GCM). We find that the model can simulate the strong seasonal variations of BrC absorption observed in China. With the help of the model and multi‐type measurement data, we estimate the contribution of BrC to the total carbonaceous aerosol absorption and the DRE of BrC in China in different seasons. This study is the first attempt to introduce an explicit BrC scheme in a GCM to estimate the DRE of BrC in China. Key Points: An explicit brown carbon (BrC) scheme is introduced in a climate model together with observations to constrain light absorption of BrCBrC contributes 19% and 12% of total carbonaceous aerosol absorption in China in winter and summer, respectivelySimulated direct radiative effects due to BrC absorption over China are 0.110 and 0.205 W m2 in the winter and summer seasons [ABSTRACT FROM AUTHOR]

We show that the winter Beaufort High (BH) index defined by sea level pressure (SLP) has a robust negative trend under the scenarios SSP5‐8.5 and SSP2‐4.5, with a reduction by about 5 hPa and 2 hPa, respectively, by the end of the 21st century. The negative trends in the BH SLP are associated with the changes in the background SLP over the Arctic basin. However, the vorticity of the winter BH tends to intensify under SSP5‐8.5, but shows no robust increase under SSP2‐4.5. The intensification is associated with the enhanced ridge over the Western Arctic. Therefore, it is necessary to take into account the dynamic aspects of the BH, such as vorticity. Based on this assessment, under the most likely emissions scenario, the winter BH is likely to weaken through the 21st century, in terms of SLP, but shows no robust changes in term of vorticity. Plain Language Summary: We used the latest IPCC global climate model outputs to investigate the wintertime changes in the Beaufort High (BH). Under the climate change scenarios, during winter, the sea level pressure (SLP) over the BH region is expected to decrease. The SLP reduction is up to 5 hPa by the end‐of‐the‐century. Thus, the BH seems to become weaker. However, its vorticity tends to increase under SSP5‐8.5, mostly due to the intensification of the ridge over the western North America. Key Points: The Beaufort High (BH) index defined by sea level pressure (SLP) decreases under warming climate scenariosMeanwhile, the BH vorticity tends to intensify due to the change in the ridge over the Western ArcticBesides the SLP, impact studies of the BH on the Arctic Ocean need to consider its dynamics such as vorticity [ABSTRACT FROM AUTHOR]

Sea ice regulates heat exchange between the ocean and atmosphere in Earth's polar regions. The thermal conductivity of sea ice governs this exchange, and is a key parameter in climate modelling. However, it is challenging to measure and predict due to its sensitive dependence on temperature, salinity and brine microstructure. Moreover, as temperature increases, sea ice becomes permeable, and fluid can flow through the porous microstructure. While models for thermal diffusion through sea ice have been obtained, advective contributions to transport have not been considered theoretically. Here, we homogenize a multiscale advection–diffusion equation that models thermal transport through porous sea ice when fluid flow is present. We consider two-dimensional models of convective flow and use an integral representation to derive bounds on the thermal conductivity as a function of the Péclet number. These bounds guarantee enhancement in the thermal conductivity due to the added flow. Further, we relate the Péclet number to temperature, making these bounds useful for global climate models. Our analytic approach offers a mathematical theory which can not only improve predictions of atmosphere–ice–ocean heat exchanges in climate models, but can provide a theoretical framework for a range of problems involving advection–diffusion processes in various fields of application. [ABSTRACT FROM AUTHOR]

Modeling atmospheric black carbon (BC) aerosol optical properties remains largely uncertain due to their complex mixing states, nonsphericity, and heterogeneity of coating distribution. Although there exist numerical models with realistic BC morphologies, these models are mostly limited to particle‐scale studies and have not been coupled to large‐scale atmospheric or climate models. In this study, a multidimensional parameterization scheme is developed by an accurate numerical algorithm for BC optical property calculation in global climate models, by incorporating their mixing state and nonspherical structure as well as heterogeneous coating distribution. The scheme was coupled and tested with the Community Atmosphere Model version 6 (CAM6) by a weighted averaging algorithm for individual particles and integration for particle ensembles. The simulation results indicate that BC morphology has a limited influence on the aerosol absorption cross section (Cabs), and the differences in Cabs between irregularly coated fractal aggregates and ideal core‐shell spherical (CS) counterparts are ∼3% on average. However, the relative positions between the BC core and coating parts may introduce Cabs variations of up to 69% as compared with the CS results. The BC mixing state introduce ∼20% relative variations in the global average aerosol absorption optical depth, which is comparable to that of heterogeneity of coating distribution and three times greater than that of particle nonsphericity. Furthermore, the normalized mean biases of modeled single scattering coalbedo (1−SSA, i.e., the ratio of absorption to extinction) compared to those observed in BC‐rich regions are reduced by 20%∼80% when applying our new parameterization in CAM6. Plain Language Summary: Atmospheric black carbon (BC) aerosols strongly absorb light, but large uncertainties remain in regard to their optical properties. Realistic and complex BC morphologies could be modeled using accurate numerical algorithms in aerosol optical simulations. However, for regional or global weather or climate simulations with large spatial‐temporal scales, models mostly simplify BC as spherical particles homogeneously mixed with other components for a sake of computational efficiency. To better account for BC optical properties with complex BC morphological and inhomogeneous properties in a large‐scale model, we develop a multidimensional optical parameterization for mixed BC based on a global atmospheric model. Our results indicate that the influence of the mixing model on the BC absorption optical depth is comparable to that of the inhomogeneous mass distribution in aerosol populations, and both are approximately three times greater than that due to nonspherical particle structure. To evaluate the performance of our aerosol optical parameterization, the in situ observations are also utilized. The results demonstrate a noteworthy improvement in modeled aerosol absorptions, as the modeled values more closely align with values derived from the observed data, both at specific locations and at the global scale. Key Points: A data set for parameterizing the optical properties of black carbon (BC) with complex inhomogeneous and nonspherical structures is builtThe biases of CAM6‐modeled aerosol absorption‐to‐extinction ratios are reduced by 20%–80% by applying our BC optical modelThe influences of the BC mixing state and heterogeneity on BC AAOD are approximately three times greater than those of BC nonsphericity [ABSTRACT FROM AUTHOR]

Despite their essential role in the high‐latitude climate, the representation of mixed‐phase clouds is still a challenge for Global Climate Models (GCMs)'s cloud schemes. In this study we propose a methodology for robustly assessing Arctic mixed‐phase cloud properties in a climate model using airborne measurements. We leverage data collected during the RALI‐THINICE airborne campaign that took place near Svalbard in August 2022 to evaluate the simulation of mid‐level clouds associated with Arctic cyclones. Simulations are carried out with the new limited‐area configuration of the ICOLMDZ model which combines the recent icosahedral dynamical core DYNAMICO and the physics of LMDZ, the atmospheric component of the IPSL‐CM Earth System Model. Airborne radar and microphysical probes measurements are then used to evaluate the simulated clouds. A comparison method has been set‐up to guarantee as much as possible the spatiotemporal co‐location between observed and simulated cloud fields. We mostly focus on the representation of ice and liquid in‐cloud contents and on their vertical distribution. Results show that the model overestimates the amount of cloud condensates and exhibits a poor cloud phase spatial distribution, with too much liquid water far from cloud top and too much ice close to it. The downward gradual increase in snowfall flux is also not captured by the model. This in‐depth model evaluation thereby pinpoints priorities for further improvements in the ICOLMDZ cloud scheme. Plain Language Summary: Mixed‐phase clouds, where ice crystals and supercooled liquid droplets coexist at sub‐zero temperatures, are ubiquitous in the polar regions. Albeit essential for the high‐latitude climate, they are still poorly represented by climate models. In this study, original measurements from the RALI‐THINICE airborne campaign, which took place in the Svalbard region in August 2022, are used to assess how the ICOLMDZ atmospheric model represents Arctic mixed phase clouds. A methodology is developed to make robust and co‐located model‐aircraft observation comparisons. In situ cloud probes and onboard radars are then used to evaluate the simulated amounts of liquid water and ice in clouds as well as the snowfall flux in deep clouds associated with the transit of Arctic cyclones. The results of the model evaluation show an overall overestimation of cloud condensates with too much liquid water far from cloud top and too much ice close to it. Key Points: Airborne RALI‐THINICE observations are used to evaluate Arctic clouds in the new limited area version of the ICOLMDZ modelA methodology has been developed to make robust and co‐located model‐observation comparisonsICOLMDZ simulates overly high amounts of liquid and ice in clouds particularly near cloud top [ABSTRACT FROM AUTHOR]

High‐latitudinal mixed‐phase clouds significantly affect Earth's radiative balance. Observations of cloud and radiative properties from two field campaigns in the Southern Ocean and Antarctica were compared with two global climate model simulations. A cyclone compositing method was used to quantify "dynamics‐cloud‐radiation" relationships relative to the extratropical cyclone centers. Observations show larger asymmetry in cloud and radiative properties between western and eastern sectors at McMurdo compared with Macquarie Island. Most observed quantities at McMurdo are higher in the western (i.e., post‐frontal) than the eastern (frontal) sector, including cloud fraction, liquid water path (LWP), net surface shortwave and longwave radiation (SW and LW), except for ice water path (IWP) being higher in the eastern sector. The two models were found to overestimate cloud fraction and LWP at Macquarie Island but underestimate them at McMurdo Station. IWP is consistently underestimated at both locations, both sectors, and in all seasons. Biases of cloud fraction, LWP, and IWP are negatively correlated with SW biases and positively correlated with LW biases. The persistent negative IWP biases may have become one of the leading causes of radiative biases over the high southern latitudes, after correcting the underestimation of supercooled liquid water in the older model versions. By examining multi‐scale factors from cloud microphysics to synoptic dynamics, this work will help increase the fidelity of climate simulations in this remote region. Plain Language Summary: The efficacy of climate prediction is largely dependent on accurately estimating Earth's energy budget in global climate models (GCMs). The Southern Ocean region has a distinct history of showing large biases in energy budget within GCMs. This region also shows complex interactions between large‐scale dynamical conditions (e.g., low‐pressure systems) and microscale processes (e.g., cloud properties). This work used two field deployments at Macquarie Island, Southern Ocean and McMurdo Station, Antarctica to understand these interactions. Observations were obtained from year‐long measurements by ground‐based instruments, which were further compared with two GCMs. The two models were found to have errors representing cloud properties at Macquarie Island (e.g., too much liquid and too little ice) and McMurdo Station (e.g., too little ice and liquid), as well as errors representing net surface longwave (terrestrial) radiation and shortwave (solar) radiation. The combination of the insufficient amounts of cloud ice and liquid in the models at McMurdo, Antarctica may be the main cause of too much solar radiation absorbed by Earth's surface over that region, which also have implications for polar ice melting and ocean circulation in that remote region. Key Points: Extratropical cyclone compositing shows more asymmetric cloud properties in eastern and western sectors at McMurdo than Macquarie IslandSimulated ice water path is too low at both sectors and both sites; liquid water path is too high (low) at Macquarie (McMurdo)Model radiation biases are affected by both cloud properties and synoptic dynamics (e.g., extratropical cyclones) [ABSTRACT FROM AUTHOR]

Odd-hydrogen (HO |$_\mathrm{x}$|⁠) species have a crucial role in regulating the chemistry of the atmosphere of Mars and are important to understand some of the most fundamental aspects regarding its atmospheric composition such as the long-term stability of CO |$_2$|⁠. Despite the key role of these species for our understanding of the Martian photochemistry, there is little observational evidence constraining their abundances. In this study, we use infrared solar occultation observations from the Atmospheric Chemistry Suite aboard the ExoMars Trace Gas Orbiter to search for spectral signatures of HO |$_2$| in the atmosphere of Mars. In our analysis of the data, we retrieve vertical profiles of pressure, temperature, and water vapour mixing ratio, but are unable to confidently detect the presence of HO |$_2$| features in the spectra. We report upper limits of 15 ppbv (5 |$\sigma$|⁠), which represents an order of magnitude improvement with respect to previous investigations. Comparing the derived upper limits with the expectations from 3-dimensional Global Climate Models, we find that approximately an order of magnitude improvement in the instrument sensitivity would be required to detect this molecule and/or constrain the models. [ABSTRACT FROM AUTHOR]

Humid heat waves pose significant risks to human health and the ecosystem. Intuitively, rainfall often alleviates extreme humid heat. However, here we show that light rain often accompanies extreme humid heat, exacerbating its frequency and intensity, especially over arid and semi-arid regions compared to no rain and moderate-to-heavy rain cases. This is because light rain does not dramatically reduce solar radiation but increases near-surface humidity through enhanced surface evaporation. The water replenishment from light rain as well as a shallower planetary boundary layer is crucial for consecutive extremes where there are commonly sporadic drizzle days amidst several rain-free days. These extremes last longer than rain-free extremes. Current global climate models (GCMs) overestimate light rain. After reducing this bias in a GCM, underestimations of humid heat waves in energy-limited regions and overestimations in water-limited regions are largely alleviated. These findings underscore the underappreciated impact of light rain on extreme humid heat. The authors show that the frequency, intensity, and duration of extreme humid heat are exacerbated by light rain through the enhancement of the total enthalpy flux and the shallowing of the planetary boundary layer. [ABSTRACT FROM AUTHOR]

ABSTRACT The warm Miocene (23.0–5.3 Ma) is considered a model case for the global climate of the near future. In view of the current coral reef crisis, studies on Miocene reefs can contribute to predictions regarding the stress resilience of today's reefs against global warming. A Late Miocene (6.2–5.5 Ma) reef coral fauna from Hispaniola Island (Caribbean) characterised by ecologically tolerant taxa was originally interpreted to reflect cool‐eutrophic waters. However, the associated planktonic foraminifer assemblage and new TEX86H–based temperature reconstructions document an oligotrophic shallow‐water environment harbouring 30–31°C. Thus, in conjunction with unusually low vertical growth rates and erratic trace element signatures of massive reef corals, the coral fauna documents critically high temperatures and a response to heat stress rather than cool upwelling. The findings demonstrate the relevance of understanding shallow‐water ecosystems from past warm periods for developing scenarios of a future, warmer world. [ABSTRACT FROM AUTHOR]

White dwarfs offer a unique opportunity to search nearby stellar systems for signs of life, but the habitable zone around these stars is still poorly understood. Since white dwarfs are compact stars with low luminosity, any planets in their habitable zone should be tidally locked, like planets around M dwarfs. Unlike planets around M dwarfs, however, habitable white dwarf planets have to rotate very rapidly, with orbital periods ranging from hours to several days. Here we use the ExoCAM global climate model to investigate the inner edge of the habitable zone around white dwarfs. Our simulations show habitable planets with ultrashort orbital periods (P ≲ 1 day) enter a "bat rotation" regime, which differs from typical atmospheric circulation regimes around M dwarfs. Bat rotators feature mean equatorial subrotation and a displacement of the surface's hottest regions from the equator toward the midlatitudes. We qualitatively explain the onset of bat rotation using shallow water theory. The resulting circulation shifts increase the dayside cloud cover and decrease the stratospheric water vapor, expanding the white dwarf habitable zone by ∼50% compared to estimates based on 1D models. The James Webb Space Telescope should be able to quickly characterize bat rotators around nearby white dwarfs thanks to their distinct thermal phase curves. Our work underlines that tidally locked planets on ultrashort orbits may exhibit unique atmospheric dynamics, and guides future habitability studies of white dwarf systems. [ABSTRACT FROM AUTHOR]

Nature-based solutions (NBS) can deliver many benefits to human wellbeing, including some crucial to climate adaptation. We quantitatively assess the global potential of NBS strategies of protection, restoration, and agroforestry by modeling global climate change mitigation and local ecosystem services (water availability, sediment retention, runoff, pollination, nitrogen retention, green water storage, and coastal protection). The strategies with the most potential to help people do not necessarily deliver the most climate change mitigation: per area of conservation action, agroforestry provides substantial benefits (>20% increase in at least one local ecosystem service) to three times more people on average than reforestation while providing less than one tenth the carbon sequestration per unit area. Each strategy delivers a different suite of ecosystem service benefits; for instance, avoided forest conversion provides a strong increase in nitrogen retention (100% increase to 72 million people if fully implemented globally) while agroforestry increases pollination services (100% increase to 3.0 billion people if fully implemented globally). One common disservice shared by all the NBS strategies modeled here is that increased woody biomass increases transpiration, reducing annual runoff and in some watersheds negatively impacting local water availability. In addition, the places with the greatest potential for climate change mitigation are not necessarily the ones with the most people. For instance, reforestation in Latin America has the greatest climate change mitigation potential, but the greatest ecosystem service benefits are in Africa. Focusing on nations with high climate mitigation potential as well as high local ecosystem service potential, such as Nigeria in the case of reforestation, India for agroforestry, and the Republic of Congo for avoided forest conversion, can help identify win-win sites for implementation. We find that concentrating implementation of these three conservation strategies in critical places, covering 5.8 million km2, could benefit 2.0 billion people with increased local ecosystem services provision. These critical places cover only 35% of the possible area of implementation but would provide 80% of the benefits that are possible globally for the selected set of ecosystem services under the NBS scenarios examined here. We conclude that targeting these critical places for protection, restoration, and agroforestry interventions will be key to achieving adaptation and human wellbeing goals while also increasing nature-based carbon mitigation. [ABSTRACT FROM AUTHOR]

In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of "too few" low clouds and "too rapid" SCT in global climate models. Plain Language Summary: Low clouds impose a strong radiative cooling effect on Earth's climate. Predicting low‐cloud fraction (LCF) is, however, challenging in global climate models (GCMs), partly due to some deficiencies in cloud parameterization schemes. Machine learning (ML) models might fill this gap as it is recognized as an efficient, economical, and accurate method to make predictions. In this study, we find that ML models (XGBoost) exhibit superior proficiency in predicting LCF regarding root mean squared errors and correlation coefficients compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. This improvement helps address one important issue of "too few" low clouds in GCMs. Furthermore, ML models demonstrate marked improvement in representing LCF variations when stratocumulus clouds transition to cumulus clouds, as opposed to too rapid decreases in LCF simulated by two CAMs and ERA5. Such findings testify to the unique role of ML models in refining the parameterization of LCF within GCMs. Key Points: Machine learning (ML) models substantially improve "too few" low‐cloud problems in the subtropics compared to traditional cloud schemesThey also show marked improvement in representing low‐cloud fraction (LCF) variations during the stratocumulus‐to‐cumulus transitionIncluding the effect of moisture source in ML models is crucial to representing spatiotemporal variations in LCF in the midlatitudes [ABSTRACT FROM AUTHOR]