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This study investigates the projected changes in the diurnal temperature range (DTR) over India and explains its considerable spatial heterogeneity from a 20‐km resolution coupled regional climate model (RSM‐ROMS) integration. The RSM‐ROMS is driven at the lateral boundaries by the Community Climate System Model version 4 (CCSM4) model. Observations reveal spatial heterogeneity in DTR trends with significant declining trends at many grid points interspersed with areas of either increasing or insignificant trends of DTR during each of the four seasons. The present‐day simulations from RSM‐ROMS show reasonable skill in simulating the daily maximum temperature (Tmax) and minimum temperature (Tmin) over India. Our results show a significant decrease in DTR over the Gangetic Plains in boreal winter and fall seasons and over southeastern India during boreal summer in the projected mid‐21st century climate under the RCP 8.5 emission scenario. The future reduction in DTR over Region‐1 (over Bihar and the eastern regions of Uttar Pradesh) during December–February (−0.86°C) and over Region‐3 (over the rain shadow regions of Peninsular India) during June–September (−0.49°C) is attributed to large changes in surface radiative fluxes, with some of the decrease in downward short wave flux attributed to an increase in high cloud cover at the time of Tmax while there is a considerable increase in downward longwave flux in the mid‐21st century climate. The enthalpy fluxes at the time of Tmax also act to reduce the rate of its warming. As a result, the warming rate of Tmax is less compared with the corresponding warming rate of Tmin, which leads to a reduction of the DTR in some regions that display a significant reduction in future climate. In contrast, Region‐2 (over Rajasthan) and Region‐4 (over northeast India) exhibit insignificant DTR changes in the mid‐21st century climate for lack of asymmetrical changes in Tmin and Tmax. [ABSTRACT FROM AUTHOR]

Motivation: Impacts of climate change on marine biodiversity are often projected with species distribution modelling using standardized data layers representing physical, chemical and biological conditions of the global ocean. Yet, the available data layers (1) have not been updated to incorporate data of the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), which comprise the Shared Socioeconomic Pathway (SSP) scenarios; (2) consider a limited number of Earth System Models (ESMs), and (3) miss important variables expected to influence future biodiversity distributions. These limitations might undermine biodiversity impact assessments, by failing to integrate them within the context of the most up‐to‐date climate change projections, raising the uncertainty in estimates and misinterpreting the exposure of biodiversity to extreme conditions. Here, we provide a significant update of Bio‐ORACLE, extending biologically relevant data layers from present‐day conditions to the end of the 21st century Shared Socioeconomic Pathway scenarios based on a multi‐model ensemble with data from CMIP6. Alongside, we provide R and Python packages for seamless integration in modelling workflows. The data layers aim to enhance the understanding of the potential impacts of climate change on biodiversity and to support well‐informed research, conservation and management. Main Types of Variable Contained: Surface and benthic layers for, chlorophyll‐a, diffuse attenuation coefficient, dissolved iron, dissolved oxygen, nitrate, ocean temperature, pH, phosphate, photosynthetic active radiation, total phytoplankton, total cloud fraction, salinity, silicate, sea‐water direction, sea‐water velocity, topographic slope, topographic aspect, terrain ruggedness index, topographic position index and bathymetry, and surface layers for air temperature, mixed layer depth, sea‐ice cover and sea‐ice thickness. Spatial Location and Grain: Global at 0.05° resolution. Time Period and Grain: Decadal from present‐day to the end of the 21st century (2000–2100). Major Taxa and Level of Measurement: Marine biodiversity associated with surface and epibenthic habitats. Software Format: A package of functions developed for Python and R software. [ABSTRACT FROM AUTHOR]

1976 was one of the most acute droughts in the UK, exceptional due to the compounding effects of low rainfall and hot summer temperatures. In this study, we provide a multiperspective view of the likelihood of a 1976‐like compound event occurring now and into the future. We find a high level of consistency in the messages emerging across a range of different approaches and climate modelling tools, from convection‐permitting climate projections to decadal hindcasts and global coupled‐model attribution ensembles. 1976 summer average temperatures remain, at the time of writing, amongst the highest on record, but with warming are becoming increasingly common. The nine‐month rainfall deficit to August 1976 was incredibly rare. Analysis here indicates that compound extremes like 1976 are expected to occur on time‐scales of hundreds to thousands of years in the present‐day climate, decreasing slightly into the future. The probability remains very small even if we account for favourable sea surface temperatures and atmospheric circulation that occurred in 1976. Similar but less severe events with a 1% chance of occurring in the present day are five times more frequent from the 2040s under RCP8.5. The occurrence of such compound events is significantly (up to an order of magnitude) higher than expected if temperature and rainfall extremes occurred independently. In general, differences in likelihood estimates between approaches can begin to be understood from how dependence between variables is handled, differences in bias correction, and different levels of conditioning (i.e., the probability given particular atmospheric or ocean states). The appropriate choice of conditioning very much depends on the question being asked and its unconscious use may lead to apparent contradictions. Parallels can be drawn between 1976 and the recent summer of 2022, and results here suggest that with hotter summers we should be prepared for more severe droughts like 1976 in the future. [ABSTRACT FROM AUTHOR]

In this study, we use the Whole Atmosphere Community Climate Model, forced by present-day atmospheric composition and coupled to a Slab Ocean Model, to simulate the state of the climate under grand solar minimum forcing scenarios. Idealized experiments prescribe time-invariant solar irradiance reductions that are either uniform (percentage-wise) across the total solar radiation spectrum (TOTC) or spectrally localized in the ultraviolet (UV) band (SCUV). We compare the equilibrium condition of these experiments with the equilibrium condition of a control simulation, forced by perpetual solar maximum conditions. In SCUV, we observe large stratospheric cooling due to ozone reduction. In both the Northern Hemisphere (NH) and the Southern Hemisphere (SH), this is accompanied by a weakening of the polar night jet during the cold season. In TOTC, dynamically induced polar stratospheric cooling is observed in the transition seasons over the NH, without any ozone deficit. The global temperature cooling values, compared with the control climate, are 0.55 ± 0.03 K in TOTC and 0.39 ± 0.03 K in SCUV. The reductions in total meridional heat transport outside of the subtropics are similar in the two experiments, especially in the SH. Despite substantial differences in stratospheric forcing, similarities exist between the two experiments, such as cloudiness; meridional heating transport in the SH; and strong cooling in the NH during wintertime, although this cooling affects two different regions, namely, North America in TOTC and the Euro–Asian continent in SCUV. [ABSTRACT FROM AUTHOR]

The aim of this work is to develop a new estuarine classification attending to their vertical structure by examining the advantages and disadvantages of the existing classifications. For this purpose, we reviewed the main classifications, finding that most of them analyze the entire estuary as a unique water body without considering the spatiotemporal variability of the mixing zone in estuaries. Furthermore, the proposed classifications require the calculation of parameters that are not easily measurable, such as tidal current amplitude. Thus, we developed a new classification based on density profile slopes of the water column, which has been correlated to the potential energy anomaly. To test this classification, the proposed method was applied to the Suances estuary (Spain) during the year 2020 and to analyze the potential estuarine modifications under four climate change projections (RCP 4.5 and 8.5 for the years 2050 and 2100). To carry out this study, a calibrated and validated high-resolution horizontal and vertical 3D model was used. The application showed a high variability in the vertical structure of the estuary due to the tide and river. According to the proposed classification, the well mixed category was predominant in the estuary in 2020 and tended to grow in the projections of climate change. As a result, the fully mixed and weakly stratified mixing classes were reduced in the projected scenarios due to a decrease of external forcing, such as river flow and sea level rise. Furthermore, areas classified as stratified tended to move upstream of the estuary. [ABSTRACT FROM AUTHOR]

Lakes are sensitive indicators of climate change as freshwater requires temperatures below 0 °C to freeze. Here, we used 34-year records for 74 lakes distributed across the Midwestern and Northeastern United States to ask the following: (i) Which physical factors affect lake ice phenology in the Northern United States?; (ii) Can an empirical statistical modelling approach be used to effectively predict ice phenology across the morphologically diverse lakes of the Northern United States?; and (iii) How much ice is forecasted to be lost in response to climate change? We find that our study lakes require 19 days with air temperatures below 0 °C to freeze, ranging from 4 days for small lakes to 53 days for larger lakes. To thaw, lakes require 22 days with air temperatures above 0 °C, ranging from 8 to 33 days. We find that 64% of the variation in ice-on dates is explained by air temperatures, and the remaining 36% of variation is explained by lake morphology, primarily mean depth. For ice-off dates, 80–90% of the variation is explained by air temperatures. By the end of the century in response to climate change, these lakes may lose 43 days of ice cover, although ranging from 12 days of less ice cover to no ice cover at all. Understanding the drivers of variability in ice phenology for lakes within regions found to be highly sensitive to climate change will promote our understanding of ice cover and ice loss, and also the widespread ecological ramifications associated with ice loss. [ABSTRACT FROM AUTHOR]

An important indicator of climate change is the diurnal temperature range (DTR), defined as the difference between the daily maximum and daily minimum air temperature. This study aims to investigate the DTR distribution in European cities of different background climates in relation to the season of the year, climate class and latitude, as well as its response to exceptionally hot weather. The analysis is based on long-term observational records (1961–2019) coupled with Regional Climate Model (RCM) data in order to detect any projected DTR trends by the end of the 21st century under intermediate and high emission greenhouse gases (GHGs) scenarios. The analysis reveals marked variations in the magnitude of DTR values between the cities, on the one hand, and distinct patterns of the DTR distribution according to the climate class of each city, on the other. The results also indicate strong seasonal variability in most of the cities, except for the Mediterranean coastal ones. DTR is found to increase during hot days and heat wave (HW) days compared to summer normal days. High latitude cities experience higher increases (3.7 °C to 5.7 °C for hot days, 3.1 °C to 5.7 °C for HW days) compared to low latitude cities (1.3 °C to 3.6 °C for hot days, 0.5 °C to 3.4 °C for HW days). The DTR is projected to significantly decrease in northernmost cities (Helsinki, Stockholm, Oslo), while it is expected to significantly increase in Madrid by the end of the 21st century under both the intermediate- and high-emission scenarios, due to the asymmetric temperature change. The asymmetrical response of global warming is more pronounced under the high-emission scenario where more cities at higher latitudes (Warsaw, Berlin, Rotterdam) are added to those with a statistically significant decrease in DTR, while others (Bucharest, Nicosia, Zurich) are added to those with an increase in DTR. [ABSTRACT FROM AUTHOR]

The large, iconic nests constructed by social species are engineered to create internal conditions buffered from external climatic extremes, to allow reproduction and/or food production. Nest-inhabiting eusocial Macrotermitinae (Blattodea: Isoptera) are outstanding palaeo-tropical ecosystem engineers that evolved fungus-growing to break down plant matter ca 62 Mya; the termites feed on the fungus and plant matter. Fungus-growing ensures a constant food supply, but the fungi need temperature-buffered, high humidity conditions, created in architecturally complex, often tall, nest-structures (mounds). Given the need for constant and similar internal nest conditions by fungi farmed by different Macrotermes species, we assessed whether current distributions of six African Macrotermes correlate with similar variables, and whether this would reflect in expected species' distribution shifts with climate change. The primary variables explaining species' distributions were not the same for the different species. Distributionally, three of the six species are predicted to see declines in highly suitable climate. For two species, range increases should be small (less than 9%), and for a single species, M. vitrialatus, 'very suitable' climate could increase by 64%. Mismatches in vegetation requirements and anthropogenic habitat transformation may preclude range expansion, however, presaging disruption to ecosystem patterns and processes that will cascade through systems at both landscape and continental scales. This article is part of the theme issue 'The evolutionary ecology of nests: a cross-taxon approach'. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) storage and redistribution across the landscape (through erosion and deposition) are linked to soil physicochemical properties and can affect soil quality. However, the spatial and temporal variability of soil erosion and SOC remains uncertain. Whether soil redistribution leads to SOC gains or losses continues to be hotly debated. These considerations cannot be modelled using conventional soil carbon models and digital soil mapping. This paper presents a coupled-model combining RothPC-1 which considers soil carbon (C) down to 1 m and a soil redistribution model. The soil redistribution component is based on a cellular automata technique using the multi-direction flow (FD8) algorithm. With the optimized input values based on land use, we simulated SOC changes upon soil profiles to 1 m across the Lower Hunter Valley area (11,300 ha) in New South Wales, Australia from the 1970s to 2016. Results were compared to field observations and showed that erosion was predicted mostly in upslope areas and deposition in low-lying areas. We further simulated SOC trends from 2017 until ~2045 in the area under three climate scenarios and five land use projections. The variation in the magnitude and direction of SOC change with different projections shows that the main factors influencing SOC changes considering soil redistribution are climate change which controlled the trend of SOC stocks, followed by land use change. Neglecting soil erosion in carbon models could lead to an overestimation of SOC stocks. This paper provides a framework for incorporating soil redistribution into the SOC dynamics modelling and also postulates the thinking that soil erosion is not just a removal process by surface runoff. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the performance of 46 global climate models (GCMs) from the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and temperature, and select the best performing GCMs for future projection across China and three major river basins. This study uses four shared socioeconomic pathways (SSPs), namely SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 relative to the base period (1961–2014). Initially, 46 models were evaluated across China employing an improved Taylor diagram method. Based on relatively better performance, 10 best‐performing models (TBMs) were selected out of 46 models for further evaluation. The results show that historical temperature was well reproduced by CMIP6 over the study regions with a high correlation coefficient (CC). All the TBMs produced good CC ranging from 0.8 to 0.99 presenting the precipitation and temperature distribution well. Meanwhile, EC‐Earth3 and EC‐Earth3‐Veg well simulated the precipitation and temperature amounts as well as trends over selected three river basins. The multimodel ensemble mean (MME) underestimates temperature over China and selected three basins with bias values of −0.53, −0.21, −0.91, and −0.68°C, respectively. In contrast, MEM overestimated the simulated precipitation with the amount of 27.7, 32.4, 21.0, and 104.6% across China and selected three basins. During future projections, increased precipitation and temperature trends are projected over three selected river basins as well as all across China. The increasing trend of future precipitation over China under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios are 0.65, 0.86, 1.29, and 0.76 mm·a−1, whereas, the increasing trend of temperature is 0.008, 0.028, 0.050, and 0.065°C·a−1, respectively. Comparatively, the greater the radiation force, the higher projected increases in precipitation and temperature across China and three major river basins were observed. The extent of CMIP6 models over the target region and its river basins calls for further deep assessment of the attribution and possible implementation of robust methods that can accurately simulate the observed patterns for future practice. [ABSTRACT FROM AUTHOR]

The multi-model ensembles like CMIP5 or CMIP6 provide a tool to analyze structural uncertainty of climate simulations. Currently developed regional and local climate change scenarios for the Czech Republic assess the uncertainty based on state-of-the-art Global Climate Model (GCM) and Regional Climate Model (RCM) ensembles. Present study focuses on multi-model spread of projected changes in long-term monthly means and inter-annual variability of monthly mean minimum, mean and maximum daily air temperature and monthly mean precipitation. We concentrate in more detail on the simulation of CNRM-ESM2-1, the driving GCM for the convection permitting ALADIN-Climate/CZ simulation contributing to the local scenarios in very high resolution. For this GCM, we also analyze a mini-ensemble with perturbed initial conditions to evaluate the range of internal climate variability. The results for the Czech Republic reveal minor differences in model performance in the reference period whereas quite substantial inter-generation shift in projected future change towards higher air temperature and lower summer precipitation in CMIP6 comparing to CMIP5. One of the prominent features across GCM generations is the pattern of summer precipitation decrease over central Europe. Further, projected air temperature increase is higher in summer and autumn than in winter and spring, implying increase of thermal continentality of climate. On the other hand, slight increase of winter precipitation and tendency towards decrease of summer precipitation lead to projected decrease of ombric continentality. The end of 21st century projections also imply higher probability of dry summer periods, higher precipitation amounts in the cold half of the year and extremely high temperature in summer. Regarding the CNRM-ESM2-1, it is often quite far from the multi-model median. Therefore, we strictly recommend to accompany any analysis based on the simulation of nested Aladin-CLIMATE/CZ with proper uncertainty estimate. The range of uncertainty connected to internal climate variability based on one GCM is often quite large in comparison to the range of whole CMIP6 ensemble. It implies that when constructing climate change scenarios for the Central Europe region, attention should be paid not only to structural uncertainty represented by inter-model differences and scenario uncertainty, but also to the influence of internal climate variability.

Species distribution models (SDMs) are a commonly used tool, which when combined with earth system models (ESMs), can project changes in organismal occurrence, abundance, and phenology under climate change. An often untested assumption of SDMs is that relationships between organisms and the environment are stationary. To evaluate this assumption, we examined whether patterns of distribution among larvae of four small pelagic fishes (Pacific sardine Sardinops sagax, northern anchovy Engraulis mordax, jack mackerel Trachurus symmetricus, chub mackerel Scomber japonicus) in the California Current remained steady across time periods defined by climate regimes, changes in secondary productivity, and breakpoints in time series of spawning stock biomass (SSB). Generalized additive models (GAMs) were constructed separately for each period using temperature, salinity, dissolved oxygen (DO), and mesozooplankton volume as predictors of larval occurrence. We assessed non-stationarity based on changes in six metrics: 1) variables included in SDMs; 2) whether a variable exhibited a linear or non-linear form; 3) rank order of deviance explained by variables; 4) response curve shape; 5) degree of responsiveness of fishes to a variable; 6) range of environmental variables associated with maximum larval occurrence. Across all species and time periods, non-stationarity was ubiquitous, affecting at least one of the six indicators. Rank order of environmental variables, response curve shape, and oceanic conditions associated with peak larval occurrence were the indicators most subject to change. Non-stationarity was most common among regimes defined by changes in fish SSB. The relationships between larvae and DO were somewhat more likely to change across periods, whereas the relationships between fishes and temperature were more stable. Respectively, S. sagax, T. symmetricus, S. japonicus, and E. mordax exhibited non-stationarity across 89%, 67%, 50%, and 50% of indicators. For all species except E. mordax, inter-model variability had a larger impact on projected habitat suitability for larval fishes than differences between two climate change scenarios (SSP1-2.6 and SSP5-8.5), implying that subtle differences in model formulation could have amplified future effects. These results suggest that the widespread non-stationarity in how fishes utilize their environment could hamper our ability to reliably project how species will respond to climatic change.

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

Climate change has lengthened wildfire seasons and transformed fire regimes throughout the world. Thus, capturing fuel and fire dynamics is critical for projecting Earth system processes in warmer and drier future. Recent advances in fire regime modeling have linked land surface models with fire behavior models. Such models often rely on fine surface fuels to drive fire behavior and effects, and while many models can simulate processes that control how these fuels change through time (i.e., fine fuel accumulation), fuel loading estimates remain highly uncertain, largely due to uncertainties in the algorithms controlling decomposition. Uncertainties are often amplified in climate change forecasts when initial conditions and feedbacks are not well represented. The goal of this review is to highlight fine fuel decomposition as a key uncertainty in model systems. We review the current understanding of mechanisms controlling decomposition, describe how they are incorporated into models, and evaluate the uncertainties associated with different approaches. We also use three state‐of‐the‐art land surface fire regime models to demonstrate the sensitivity of decomposition and subsequent wildfire projections to both parameter and model structure uncertainty and show that sensitivity can increase substantially under future climate warming. Given that many of the governing decomposition equations are based on individual case studies from a single location, and because key parameters are often hard coded, critical uncertainties are currently ignored. It is essential to be transparent about these uncertainties as the domain of land surface models is expanded to include the evaluation of future wildfire regimes. Plain Language Summary: Wildfire is a critical force regulating carbon retention globally. This is especially true in coniferous forests, which store more than one‐third of the Earth's terrestrial carbon. Fine, dead materials on the forest floor (i.e., fine surface fuels) play a key role in driving fire spread. Thus, modeling the role of fire in Earth system processes requires reliable estimates of fine surface fuel loading and projections of how it will change over time (i.e., fine fuel accumulation). To accomplish this, we need models that can account for complex interactions among climate and vegetation—including the effects of temperature and precipitation on plant growth, mortality, litterfall, and litter decay—and that link these dynamics with projections of future wildfire. Although many models are designed to simulate these processes, fuel loading estimates remain highly uncertain. In this paper, we review the current understanding of mechanisms controlling fine fuel accumulation, describe how these mechanisms are represented in models, and evaluate the uncertainties associated with different approaches. We conclude with recommendations for future research needed to better model how climate change will influence fuels, wildfire, and carbon retention. Key Points: Developing land surface models for climate‐fire interactions requires estimating and overcoming uncertainty in fuel accumulation processesModels that simulate fuel accumulation differ in how they parameterize and represent fuel decomposition; assumptions are often hard codedSensitivity to parameter and model structure uncertainty increases with climate warming and decreases with increasing precipitation [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) storage and redistribution across the landscape (through erosion and deposition) are linked to soil physicochemical properties and can affect soil quality. However, the spatial and temporal variability of soil erosion and SOC remains uncertain. Whether soil redistribution leads to SOC gains or losses continues to be hotly debated. These considerations cannot be modelled using conventional soil carbon models and digital soil mapping. This paper presents a coupled-model combining RothPC-1 which considers soil carbon (C) down to 1 m and a soil redistribution model. The soil redistribution component is based on a cellular automata technique using the multi-direction flow (FD8) algorithm. With the optimized input values based on land use, we simulated SOC changes upon soil profiles to 1 m across the Lower Hunter Valley area (11,300 ha) in New South Wales, Australia from the 1970s to 2016. Results were compared to field observations and showed that erosion was predicted mostly in upslope areas and deposition in low-lying areas. We further simulated SOC trends from 2017 until ~2045 in the area under three climate scenarios and five land use projections. The variation in the magnitude and direction of SOC change with different projections shows that the main factors influencing SOC changes considering soil redistribution are climate change which controlled the trend of SOC stocks, followed by land use change. Neglecting soil erosion in carbon models could lead to an overestimation of SOC stocks. This paper provides a framework for incorporating soil redistribution into the SOC dynamics modelling and also postulates the thinking that soil erosion is not just a removal process by surface runoff.