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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]

We present a dataset of daily, bias-adjusted temperature and precipitation projections for continental Africa based on a large ensemble of regional climate model simulations, which can be useful for climate change impact studies in several sectors. We provide guidance on the benefits and caveats of using the dataset by investigating the effect of bias-adjustment on impact-relevant indices (both their future absolute value and change). Extreme threshold-based temperature indices show large differences between original and bias-adjusted values at the end of the century due to the general underestimation of temperature in the present climate. These results indicate that when biases are accounted for, projected risks of extreme temperature-related hazards are higher than previously found, with possible consequences for the planning of adaptation measures. Bias-adjusted results for precipitation indices are usually consistent with the original results, with the median change preserved for most regions and indices. The interquartile and full range of the original model ensemble is usually well preserved by bias-adjustment, with the exception of maximum daily precipitation, whose range is usually greatly reduced by the bias-adjustment. This is due to the poor simulation and extremely large model range for this index over the reference period; when the bias is reduced, most models converge in projecting a similar change. Finally, we provide a methodology to select a small subset of simulations that preserves the overall uncertainty in the future projections of the large model ensemble. This result can be useful in practical applications when process-based impact models are too expensive to be run with the full ensemble of model simulations. [ABSTRACT FROM AUTHOR]

Water-resources planners use regional water management models (WMMs) to identify vulnerabilities to climate change. Frequently, dynamically downscaled climate inputs are used in conjunction with land-surface models (LSMs) to provide hydrologic streamflow projections, which serve as critical inputs for WMMs. Here, we show how even modest projection errors can strongly affect assessments of water availability and financial stability for irrigation districts in California. Specifically, our results highlight that LSM errors in projections of flood and drought extremes are highly interactive across timescales, path-dependent, and can be amplified when modeling infrastructure systems (e.g., misrepresenting banked groundwater). Common strategies for reducing errors in deterministic LSM hydrologic projections (e.g., bias correction) can themselves strongly distort projected climate vulnerabilities and misrepresent their inferred financial consequences. Overall, our results indicate a need to move beyond standard deterministic climate projection and error management frameworks that are dependent on single simulated climate change scenario outcomes. [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.

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

Abstract Land use and climate changes both affect terrestrial ecosystems. Here, we used three combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1xRCP26, SSP3xRCP60, and SSP5xRCP85) as input to three dynamic global vegetation models to assess the impacts and associated uncertainty on several ecosystem functions: terrestrial carbon storage and fluxes, evapotranspiration, surface albedo, and runoff. We also performed sensitivity simulations in which we kept either land use or climate (including atmospheric CO2) constant from year 2015 on to calculate the isolated land use versus climate effects. By the 2080–2099 period, carbon storage increases by up to 87 ± 47 Gt (SSP1xRCP26) compared to present day, with large spatial variance across scenarios and models. Most of the carbon uptake is attributed to drivers beyond future land use and climate change, particularly the lagged effects of historic environmental changes. Future climate change typically increases carbon stocks in vegetation but not soils, while future land use change causes carbon losses, even for net agricultural abandonment (SSP1xRCP26). Evapotranspiration changes are highly variable across scenarios, and models do not agree on the magnitude or even sign of change of the individual effects. A calculated decrease in January and July surface albedo (up to −0.021 ± 0.007 and −0.004 ± 0.004 for SSP5xRCP85) and increase in runoff (+67 ± 6 mm/year) is largely driven by climate change. Overall, our results show that future land use and climate change will both have substantial impacts on ecosystem functioning. However, future changes can often not be fully explained by these two drivers and legacy effects have to be considered.

Aim: Forecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species' distribution response to changing climate. In this study, we provide an overview of common modelling practices in the field and assess the reliability of model predictions using a virtual species approach. Location: Global. Methods: We first review papers published between 2015 and 2019. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and random forest) to assess the estimated and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. Results: Most SDM papers relied on single models (65%) and small samples (N < 50, 62%), used presence‐only data (85%), binarized models' output (74%) and used a split‐sample validation (94%). Our simulation reveals that the split‐sample validation tends to be over‐optimistic compared to the real performance, whereas spatial block validation provides a more honest estimate, except when datasets are environmentally biased. The binarization of predicted probabilities of presence reduces models' predictive ability considerably. Sample size is one of the main predictors of the real model accuracy, but has little influence on estimated accuracy. Finally, the inclusion of ecologically irrelevant predictors and the violation of modelling assumptions increases estimated accuracy but decreases real accuracy of model projections, leading to biased estimates of range contraction and expansion. Main conclusions: Our ability to predict future species distribution is low on average, particularly when models' predictions are binarized. A robust validation by spatially independent samples is required, but does not rule out inflation of model accuracy by assumption violation. Our findings call for caution in the application and interpretation of SDM projections under different climates. [ABSTRACT FROM AUTHOR]

The future climate change information plays key role for planning adaptation and mitigation strategy. In this study, the combination of the widely employed statistical downscaling model (SDSM) and two CMIP5 models, namely MPI-ESM-LR and CNRM-CM5, was used to generate the future projection of maximum and minimum temperature (Tmax and Tmin) under RCP8.5 and RCP 2.6 emission scenarios within a period of 2011 to 2100 over the Haihe Basin. The historical ground observations (daily maximum and minimum temperature) during 1971 ~2000 was employed to calibrate the SDSM models. Results showed that:(l) The SDSM model had a good ability to reproduce the daily and monthly mean Tmax and Tmin in the basin; (2) For the historical reproduction of Tmax and Tmin, the performance of CNRM-CM5 was a little worse than that of MPI-ESM-LR. (3) The change in annual mean Tmax and Tmin under the two scenarios for all evaluation periods will increase and magnitude of Tmax will be higher than Tmin. ( 4) The increase in magnitude for the weather stations in the mountains and along the coastline will be remarkably obvious. (5) The future annual Tmax and Tmin will keep a significant upward trend under RCP8.5 scenarios over the whole projection period and the magnitude will be 0.37 °C and 0.39 °C per decade, r espectively; the future annual Tmax and Tmin will increase in 2020s and then decrease in 2050s and 2070s, and the magnitude will be 0.01°C and 0.01°C per decade, respectively. The related results could provide an insight into the mitigation measure for adverse effect of future climate change on the regional ecological environment. [ABSTRACT FROM AUTHOR]

In this study, projected changes in mean northeast monsoon (NEM) rainfall and associated extreme rainfall and temperature events, over peninsular India (PI) and its six subdivisions, are quantified. High‐resolution dynamically downscaled simulations of the Weather Research and Forecasting (WRF) regional climate model driven by the boundary conditions from the Community Climate System Model version 4 (CCSM4) model (WRF‐CCSM4) are compared with statistically downscaled simulations of NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP). Over PI, these downscaled simulations show low bias in mean NEM rainfall (≤ − 0.44 mm·day−1) and high pattern correlation coefficient (≥0.75), giving confidence in their future projections. Under future warming over PI, both downscaled simulations project future significant enhancement in NEM rainfall with WRF‐CCSM4 projecting 1.98 mm·day−1 (83.78% change with respect to the present‐day mean) whereas the multimodel ensemble (MME) of eight NEX‐GDDP models project 0.67 ± 0.58 mm·day−1 (19.78%) by the midddle of the century and 1.42 ± 0.97 mm·day−1 (42.76%) by the end of the century. Analysis of extreme rainfall events shows that WRF‐CCSM4 projects future enhancement (reduction) in extreme rainfall (R95p) days over 91.4% (8.6%) of grid‐points over PI. In future, coastal areas of Karnataka and Andhra Pradesh will likely experience increased extreme rainfall occurrence by more than 25 days and 15–20 days respectively. Projected future enhancement in the mean and extreme NEM rainfall is attributed to the increased precipitable water under a warming climate. Future projection of extreme temperature indices shows an increase in minimum and maximum temperatures over PI during the NEM season. Over PI, future winter nights and days are found to be warmer than those in the present day and the temperature change in future winter nights is found to be larger than that in winter days. This climate change information would help decision‐makers in evaluating existing policies and devising revised policies to reduce risk due to climate change. [ABSTRACT FROM AUTHOR]

To meet increasing demand for information on future drought hazard to help Australia build resilience and preparedness under a changing climate, we developed new information on drought projections for Australia and four sub-regions based on the natural resources management (NRM) zones. The information reported here includes: two drought indices (the Standardised Precipitation Index, SPI, and the Standardised Soil Moisture Index, SSMI); four drought metrics (percent time spent in droughts, mean drought duration, mean drought frequency, and mean drought intensity); and two drought categories (drought and extreme drought). The projections are developed from CMIP5 global climate model simulations of rainfall and soil moisture for the historical (1900–2005) and future (2006–2100) climates.The multi-model results project significant increases in all the drought hazard metrics, except frequency, with larger changes in the SSMI compared to SPI. The more severe drought hazard under climate change is apparent over a larger area than previously indicated, particularly in southern and eastern Australia. Although the majority of modelling results indicate more severe drought conditions, the range in the results is large, mainly because of the uncertainty in the global climate model rainfall projections. A projected decrease in rainfall results in a projected increase in drought severity (which is further enhanced by the increase in potential evapotranspiration), and a projected increase in rainfall results in a projected decrease in drought severity (moderated by the increase in potential evapotranspiration). The assessment of the ability of models to reproduce historical observations does not show clusters of models that best simulate all the different drought metrics. Unlike previously assumed, the results show that the models that best reproduce the observed rainfall are not necessarily best in simulating the drought metrics. For this reason, all the models are used here to estimate the multi-model median and range of results. The large uncertainty in the projections can be confusing to end users and present challenges in adapting to climate change. The presentation and communication of projections here will also go some way towards overcoming this challenge.

Modelling the probable effect of global warming on precipitation over the northern sub-Himalayan region is very important to ensure sustainable water supply for Pakistan. The aim of the study is to develop statistical downscaling models for the projection of precipitation using the outputs of Coupled Model Intercomparison Project Phase 5 global circulation models and using future scenarios. The models were developed considering the Global Precipitation Climatology Centre precipitation data as model predictands. The downscaling models were developed using non-local model output statistics approach based on support vector machine (SVM). Random Forest was applied to formulate multimodal ensemble (MME) for the projection of precipitation. The accuracy of models was judged using the percentage of bias, normalized root mean square error, and the modified index of agreement (md). Results showed that the SVM downscaling model simulated the temporal and spatial distributions of historical precipitation with high skills. The MME showed variations in the range of − 12.68% to 6.31%, − 9.61% to 3.45%, − 8.70% to 9.15%, and − 9.40% to 5.47% for RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. The spatial pattern of annual mean rainfall of MME revealed an expansion of high rainfall area, especially in 2070–2099 under all scenarios. [ABSTRACT FROM AUTHOR]

Statistical downscaling (SD) and bias adjustment (BA) methods are routinely used to produce regional to local climate change projections from coarse global model outputs. The suitability of these techniques depends on the particular application of interest and, especially, on the required spatial resolution. Whereas SD is appropriate for local (e.g., gauge) resolution, BA may be a good alternative when the gap between the predictor and predictand resolution is small. However, the different sources of uncertainty affecting SD such as reanalysis uncertainty, the choice of suitable predictors, climate model, and/or statistical approach may yield implausible projections in particular situations for which BA techniques may offer a compromise alternative, even for local resolution. In this work, we consider a case study with 41 rain gauges over Malawi and show that, despite producing similar results for a historical period, the use of different predictors may lead to large differences in the future projections obtained from SD methods. For instance, using temperature T (specific humidity Q) produces much drier (wetter) conditions than those projected by the raw global models for the target area. We demonstrate that this can be partially alleviated by substituting T+Q by relative humidity R, which simultaneously accounts for both water availability and temperature, and yields regional projections more compatible with the global one. Nevertheless, large local differences still persist, lacking a physical interpretation. In these situations, the use of simpler approaches such as empirical BA may lead to more plausible (i.e., more consistent with the global model) projections. [ABSTRACT FROM AUTHOR]

Changes in the atmospheric composition alter the magnitude and partitioning between the downward propagating solar and atmospheric longwave radiative fluxes heating the Earth's surface. These changes are computed by radiative transfer codes in Global Climate Models and measured with high precision at surface observation networks. Changes in radiative heating signify changes in the global surface temperature and hydrologic cycle. Here, we develop a conceptual framework using an Energy Balance Model to show that first‐order changes in the hydrologic cycle are mainly associated with changes in solar radiation, while those in surface temperature are mainly associated with changes in atmospheric longwave radiation. These insights are used to explain a range of phenomena including observed historical trends, biases in climate model output, and the intermodel spread in climate change projections. These results may help identify biases in future generations of climate models. Key Points: Surface temperature and precipitation are asymmetrically related to the solar and longwave surface radiative heating fluxesOur results, derived from an idealized model, explain observed climate trends, GCM biases, and spread in climate model projectionsThese results are expected to apply across future generations of climate models [ABSTRACT FROM AUTHOR]

This study assessed the regional climate models (RCMs) employed in the Coordinated Regional climate Downscaling Experiment (CORDEX) South Asia framework to investigate the qualitative aspects of future change in seasonal mean near surface air temperature and precipitation over the Hindu Kush Himalayan (HKH) region. These RCMs downscaled a subset of atmosphere ocean coupled global climate models (AOGCMs) in the Coupled Model Intercomparison Project phase 5 (CMIP5) to higher 50 km spatial resolution over a large domain covering South Asia for two representation concentration pathways (RCP4.5 and RCP8.5) future scenarios. The analysis specifically examined and evaluated multi-model and multi-scenario climate change projections over the hilly sub-regions within HKH for the near-future (2036–2065) and far-future (2066–2095) periods. The downscaled multi-RCMs provide relatively better confidence than their driving AOGCMs in projecting the magnitude of seasonal warming for the hilly sub-region within the Karakoram and northwestern Himalaya, with higher projected change of 5.4 °C during winter than of 4.9 °C during summer monsoon season by the end of 21st century under the high-end emissions (RCP8.5) scenario. There is less agreement among these RCMs on the magnitude of the projected warming over the other sub-regions within HKH for both seasons, particularly associated with higher RCM uncertainty for the hilly sub-region within the central Himalaya. The downscaled multi-RCMs show good consensus and low RCM uncertainty in projecting that the summer monsoon precipitation will intensify by about 22% in the hilly sub-region within the southeastern Himalaya and Tibetan Plateau for the far-future period under the RCP8.5 scenario. There is low confidence in the projected changes in the summer monsoon and winter season precipitation over the central Himalaya and in the Karakoram and northwestern Himalaya due to poor consensus and moderate to high RCM uncertainty among the downscaled multi-RCMs. Finally, the RCM related uncertainty is found to be large for the projected changes in seasonal temperature and precipitation over the hilly sub-regions within HKH by the end of this century, suggesting that improving the regional processes and feedbacks in RCMs are essential for narrowing the uncertainty, and for providing more reliable regional climate change projections suitable for impact assessments in HKH region.

Lake Ontario ice conditions are statistically linked to regional temperatures recorded in Toronto, during the most recent climate normal (1980/81–2009/10). A metric was developed to capture the net melting effect of average winter temperatures to characterize lake ice conditions, referred to as Net Melting-Degree Days (NMDD). This metric was able to account for 78% of lake ice interannual variability (R2 = 0.783, P < 0.001). Based on NMDD parameters, current lake ice conditions were characterized in four ways: heavy, moderate, light and very light. Lake Ontario ice conditions were reconstructed to create a hindcast for the span of the instrumental temperature record (1840/41–1979/80). Based on a decadal analysis, heavy ice seasons decreased significantly (R2 = 0.658, P < 0.001) from the 1840s to the 2000s, declining from an average of 6 heavy ice seasons per decade during the most distant climate normal (1840s to 1960s) to an average of only 1 heavy ice season per decade during the most recent climate normal (1980s to 2000s). Finally, lake ice conditions are projected to the end of the 21st century, using an optimal ensemble of Global Climate Model outputs for two different climate change scenarios (RCP4.5, RCP8.5). Heavy ice seasons no longer occur as early as the 2050s under both RCP4.5 and RCP8.5. Whereas, very light ice seasons go from being an extreme in the baseline period (10%), to the dominant characterization of Lake Ontario ice conditions by the 2080s, for both RCP4.5 (73%) and RCP8.5 (100%). [ABSTRACT FROM AUTHOR]

Tropical regions are characterized by hydrological extreme events, which are likely to be exacerbated by climate change. Therefore, quantifying the extent to which climate change may damage a hydrological system becomes crucial. This paper aims to evaluate the findings from previous research on projected impacts of climate change on hydrological systems located in regions bounded by the Tropic of Cancer and the Tropic of Capricorn. It intends to provide an in-depth understanding of the climatic conditions, applied approaches, climate change impacts on future streamflow, and measures to reduce prediction uncertainty in the tropics. The review revealed that there is a significant variation in the magnitude of climate change impacts on streamflow in the tropics. The reason for the inconsistent trend prediction is that projections are heavily dependent on the trajectory of greenhouse gas emissions, climate model structural differences, and uncertainty of downscaling methods and hydrological models. Therefore, to minimize the uncertainty and maximize confidence in streamflow projections, it is essential to apply multi-member model ensembles and to clarify the adaptation strategy (coping, adjusting, or transforming). [ABSTRACT FROM AUTHOR]

In the second part of this study, possible impacts of climate change on Surface Solar Radiation (SSR) in Southern Africa (SA) are evaluated. We use outputs from 20 regional climate simulations from five Regional Climate Models (RCM) that participate in the Coordinated Regional Downscaling Experiment program over the African domain (CORDEX-Africa) along with their 10 driving Global Climate Models (GCM) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model mean projections of SSR trends are consistent between the GCMs and their nested RCMs. However, this consistency is not found for each GCM/RCM setup. Over the centre of SA, GCMs and RCMs project a statistically significant increase in SSR by 2099 of about + 1 W/m2 per decade in RCP4.5 (+ 1.5 W/m2 per decade in RCP8.5) during the DJF season in their multi-model means. Over Eastern Equatorial Africa (EA-E) a statistically significant decrease in SSR of about − 1.5 W/m2 per decade in RCP4.5 (− 2 W/m2 per decade in RCP8.5) is found in the ensemble means in DJF, whereas in JJA SSR is predicted to increase by about + 0.5 W/m2 per decade under RCP4.5 (+ 1 W/m2 per decade in RCP8.5). SSR projections are fairly similar between RCP8.5 and RCP4.5 before 2050 and then the differences between those two scenarios increase up to about 1 W/m2 per decade with larger changes in RCP8.5 than in RCP4.5 scenario. These SSR evolutions are generally consistent with projected changes in Cloud Cover Fraction over SA and may also related to the changes in atmosphere water vapor content. SSR change signals emerge earlier out of internal variability estimated from reanalyses (European Centre for Medium-Range Weather Forecasts Reanalysis ERA-Interim, ERAIN) in DJF in RCMs than in GCMs, which suggests a higher sensitivity of RCMs to the forcing RCP scenarios than their driving GCMs in simulating SSR changes. Uncertainty in SSR change projections over SA is dominated by the internal climate variability before 2050, and after that model and scenario uncertainties become as important as internal variability until the end of the 21st century. [ABSTRACT FROM AUTHOR]

Land use and climate changes both affect terrestrial ecosystems. Here, we used three combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1xRCP26, SSP3xRCP60, and SSP5xRCP85) as input to three dynamic global vegetation models to assess the impacts and associated uncertainty on several ecosystem functions: terrestrial carbon storage and fluxes, evapotranspiration, surface albedo, and runoff. We also performed sensitivity simulations in which we kept either land use or climate (including atmospheric CO2) constant from year 2015 on to calculate the isolated land use versus climate effects. By the 2080–2099 period, carbon storage increases by up to 87 ± 47 Gt (SSP1xRCP26) compared to present day, with large spatial variance across scenarios and models. Most of the carbon uptake is attributed to drivers beyond future land use and climate change, particularly the lagged effects of historic environmental changes. Future climate change typically increases carbon stocks in vegetation but not soils, while future land use change causes carbon losses, even for net agricultural abandonment (SSP1xRCP26). Evapotranspiration changes are highly variable across scenarios, and models do not agree on the magnitude or even sign of change of the individual effects. A calculated decrease in January and July surface albedo (up to −0.021 ± 0.007 and −0.004 ± 0.004 for SSP5xRCP85) and increase in runoff (+67 ± 6 mm/year) is largely driven by climate change. Overall, our results show that future land use and climate change will both have substantial impacts on ecosystem functioning. However, future changes can often not be fully explained by these two drivers and legacy effects have to be considered. Key Points: Future climate change will increase terrestrial carbon stocks, while future land use change will decrease terrestrial carbon stocksFuture climate change and land use change will also affect a range of ecosystem functions beyond carbon storageHowever, future changes in ecosystem functioning can often not be explained by future climate change and future land use change alone [ABSTRACT FROM AUTHOR]

Aim: Most projections of how climate change will affect species distributions and phenology are based on a species' most conspicuous life stage. However, not all life stages are equally sensitive to temperature. Among fishes, spawning adults often have narrower thermal tolerances than other life stages and may constrain population responses to climate change. We tested this hypothesis using data on Nassau Grouper (Epinephelus striatus), an endangered coral reef fish. Location: Greater Caribbean. Methods: Species distribution models of spawning and nonspawning adults were compared to determine which environmental variables exerted the greatest influence on grouper distribution. We calculated the thermal niche and ecological niche breadth of both life stages. An earth system model was applied to project how species distribution and phenology shift under two climate change scenarios. Results: Sea surface temperature and seasonal temperature gradients affected the distribution of both spawning and nonspawning adults, but these life stages differed in their preferred temperatures and reaction to oceanic currents. While the two life stages exhibited similar ecological niche breadth, the thermal niche of spawners was narrower. By 2081–2100, potential spawning habitat was projected to decline under a business‐as‐usual scenario by 82% relative to 1981–2000, whereas suitable habitat for nonspawners decreased by 46%. Poleward shifts in latitude occurred 3.8–4.2 times faster for spawners than nonspawners. These changes were attributed to rising temperatures, whereas hydrographical changes did not have a substantial impact. Spawning phenology changed little, with a slight contraction in spawning season but a large reduction in spawning probability across all months. Main Conclusions: A narrow thermal tolerance range among spawning fishes indicates that this life stage may be a bottleneck constraining responses to climate change. Future research should consider the reaction of each life stage to changing conditions. Conservation of E. striatus should take shifting distribution and phenology into account, as climate effects may exacerbate population declines due to fishing and reduce the efficacy of conservation measures. [ABSTRACT FROM AUTHOR]

As part of the CORDEX project, the fifth-generation Canadian Regional Climate Model (CRCM5) is used over the Arctic for climate simulations driven by reanalyses and by the MPI-ESM-MR coupled global climate model (CGCM) under the RCP8.5 scenario. The CRCM5 shows adequate skills capturing general features of mean sea level pressure (MSLP) for all seasons. Evaluating 2-m temperature (T2m) and precipitation is more problematic, because of inconsistencies between observational reference datasets over the Arctic that suffer of a sparse distribution of weather stations. In our study, we additionally investigated the effect of large-scale spectral nudging (SN) on the hindcast simulation driven by reanalyses. The analysis shows that SN is effective in reducing the spring MSLP bias, but otherwise it has little impact. We have also conducted another experiment in which the CGCM-simulated sea-surface temperature (SST) is empirically corrected and used as lower boundary conditions over the ocean for an atmosphere-only global simulation (AGCM), which in turn provides the atmospheric lateral boundary conditions to drive the CRCM5 simulation. This approach, so-called 3-step approach of dynamical downscaling (CGCM-AGCM-RCM), which had considerably improved the CRCM5 historical simulations over Africa, exhibits reduced impact over the Arctic domain. The most notable positive effect over the Arctic is a reduction of the T2m bias over the North Pacific Ocean and the North Atlantic Ocean in all seasons. Future projections using this method are compared with the results obtained with the traditional 2-step dynamical downscaling (CGCM-RCM) to assess the impact of correcting systematic biases of SST upon future-climate projections. The future projections are mostly similar for the two methods, except for precipitation. [ABSTRACT FROM AUTHOR]

Reservoir sizing is one of the most important aspects of water resources engineering as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, observed streamflow is used to stochastically generate multiple realizations of streamflow to estimate the required storage based on the Sequent Peak Algorithm (SQP). The main limitation in this approach is that the parameters of the stochastic model are purely derived from the observed record (limited to less than 80 years of data) which does not have information related to prehistoric droughts. Further, reservoir sizing is typically estimated to meet future increase in water demand, and there is no guarantee that future streamflow over the planning period will be representative of past streamflow records. In this context, reconstructed streamflow records, usually estimated based on tree ring chronologies, provide better estimates of prehistoric droughts, and future streamflow records over the planning period could be obtained from general circulation models (GCMs) which provide 30 year near-term climate change projections. In this study, we developed paleo streamflow records and future streamflow records for 30 years are obtained by forcing the projected precipitation and temperature from the GCMs over a lumped watershed model. We propose combining observed, reconstructed and projected streamflows to generate synthetic streamflow records using a Bayesian framework that provides the posterior distribution of reservoir storage estimates. The performance of the Bayesian framework is compared to a traditional stochastic streamflow generation approach. Findings based on the split-sample validation show that the Bayesian approach yielded generated streamflow traces more representative of future streamflow conditions than the traditional stochastic approach thereby, reducing uncertainty on storage estimates corresponding to higher reliabilities. Potential strategies for improving future streamflow projections and its utility in reservoir sizing and capacity expansion projects are also discussed. [ABSTRACT FROM AUTHOR]

Changes in the essential climate extremes indices and surface variables for the end of the twenty-first century are assessed in this study based on two transient climate change simulations, with and without land-use and land-cover changes (LULCC), but identical atmospheric forcing. The two simulations are performed with the 5th generation of the Canadian Regional Climate Model (CRCM5) driven by the Canadian Earth System Model for the (2006-2100)-Representative Concentration Pathway 4.5 (RCP4.5) scenario. For the simulation with LULCC, land-cover data sets are taken from the global change assessment model (GCAM) representing the RCP4.5 scenario for the period 2006-2100. LULCC in RCP4.5 scenario suggest significant reduction in cultivated land (e.g. Canadian Prairies and Mississippi basin) due to afforestation. CRCM5 climate projections imply a general warming by the end of the twenty-first century, especially over the northern regions in winter. CRCM5 projects more warm spell-days per year over most areas of the continent, and implicitly more summer days and tropical nights at the expense of cold-spell, frost and ice days whose number is projected to decrease by up to 40% by the end of the twenty-first century with respect to the baseline period 1971-2000. Most land areas north of 45°N, in all seasons, as well as the southeastern United States in summer, exhibit increases in mean precipitation under the RCP4.5 scenario. In contrast, central parts of the continent in summer and much of Mexico in all seasons show reduced precipitation. In addition, large areas of North America exhibit changes of 10 to 40% (depending on the season and geographical location) in the number of heavy precipitation days. Results also suggest that the biogeophysical effects of LULCC on climate, assessed through differences between the two simulations, lead to warmer regional climates, especially in winter. The investigation of processes leading to this response shows high sensitivity of the results to changes in albedo as a response to LULCC. Overall, at the seasonal scale, results show that intense afforestation may contribute to an additional 25% of projected changes. [ABSTRACT FROM AUTHOR]

It is crucial to define and quantify possible impacts of climate change on wildlife in order to be able to pre-adapt management strategies for nature conservation. Thus, it is necessary to assess which species might be affected by climatic changes, especially at the regional scale. We present a novel approach to estimate possible climate change induced turnovers in bird communities and apply this method to Lüneburg Heath, a region in northern Germany. By comparing species pools of future climatically analogous regions situated in France with the Lüneburg Heath species pool, we detected possible trends for alterations within the regional bird community in the course of climate change. These analyses showed that the majority of bird species in Lüneburg Heath will probably be able to tolerate the projected future climate conditions, but that bird species richness, in general, may decline. Species that might leave the community were often significantly associated with inland wetland habitats, but the proportion of inland wetlands within the regions had a significant influence on the magnitude of this effect. Our results suggest that conservation efforts in wetlands have to be strengthened in light of climate change because many species are, in principle, able to tolerate future climate conditions if sufficient habitat is available.