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Given the increasingly severe challenges of global climate change and sustainable development, accurate prediction of daily carbon dioxide (CO 2) emissions has become crucial. However, current research on real-time daily forecasts remains relatively scarce, often limited to annual predictions and point estimates. This paper proposes an innovative integrated model, based on quantile regression and Attention-Bidirectional Long Short-Term Memory (BILSTM), specifically designed for probabilistic prediction of daily carbon emissions. Testing the model on daily carbon emission data from major carbon-emitting countries including China, the United States, India, Russia, and Italy has demonstrated its superior performance across various metrics, including mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and normalized average width of prediction intervals (PINAW), compared to other benchmark models. This achievement not only highlights the model's effectiveness in predicting daily carbon emissions but also provides policymakers with a powerful tool for real-time monitoring and assessment of carbon emissions. Furthermore , the application of probabilistic forecasting methods enables decision-makers to gain a more comprehensive understanding of the potential range of future carbon emissions, providing strong support for the formulation of targeted policies. [ABSTRACT FROM AUTHOR]

Accurately estimating actual evapotranspiration (ET) across global land surface is one of the key challenges in terrestrial hydrological cycles and energy flux balance studies. Gridded ET products have the potential for application in ungauged basins, but their uncertainties are possibly large and it remains unclear which one is best for a given basin. The water balance (WB) method provides a direct estimate of basin scale ET, but it cannot be used in ungauged basins where streamflow data are unavailable. Here, we first assess the performance of ET from 10 global ET products against WB ET estimates in 43 large river basins. The paper then uses three indirect evaluation methods [Three Cornered Hat (TCH), Arithmetic Average (AA), and Bayesian Three Cornered Hat] to identify the optimal ET products without the need of prior information, and to generate fusion products combining the ET from multiple products. Using the evaluation results derived from the WB method as the reference, the results show that the three methods have great success in identifying poorer products, suggesting that they are useful in filtering poor ET products in applications. However, the ability of such methods in identifying better ET products degrades slightly. The AA fusion product, which combines ET outputs from multiple products, is marginally better than the best single ET product in many of the 43 basins. Because of its simplicity, it could be used to reduce the uncertainty in ET estimates from multiple products for ungauged basins and regions. Key Points: A comprehensive study on reducing the uncertainty in evapotranspiration (ET) estimates from 10 global ET products for application in ungauged river basinsThe indirect evaluation methods without using prior information, particularly Arithmetic Average (AA) and Bayesian Three Cornered Hat can successfully identify the poor ET productsThe AA fusion product, which combines ET outputs from multiple products, is generally marginally better than the best single ET product [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) have been widely used to predict species ranges and their future distribution under climate change scenarios, mostly using only climatic variables. An important factor that is usually neglected, is the habitat of the species that are being modelled. Even when included, it is often considered a fixed factor, but in reality, it is also subjected to changes. In this study, we assessed if this omission can lead to different projected distributions of the species. For this purpose, we applied an ensemble of SDMs, and we projected the distribution of rare bryophyte species in Scotland in the 2050s. Bryophytes are generally very climate-reliant and lend themselves to bioclimatic studies, and we selected species different grades of affinity with blanket bogs, which are threatened by climate change. Blanket bog extension was included in the model as an explanatory variable, and the models were run for three 2050s scenarios: once with the current blanket bog distribution and twice using the blanket bog distribution derived from two bioclimatic models (Lindsay modified and Blanket Bog Tree model), under the same climate change projections. The results showed some differences in the predicted future distribution of those species with a strong relationship with blanket bogs, when habitat changes were accounted for. For example, Sphagnum majus, the species with the highest affinity with blanket bog in our study, was not predicted to change its future distribution when blanket bog is held constant at the current level, but was predicted to lose up to 60% of its current suitable area when the projected loss of blanket bog is included. Our results suggest that adding future habitat changes could improve the reliability of SDMs in the first steps of planning for conservation and restoration. [ABSTRACT FROM AUTHOR]

Seasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting. [ABSTRACT FROM AUTHOR]

An emergent consequence of climate change is its potential impacts on transatlantic flight routes and durations, with studies suggesting that future optimal paths will likely lead to decreased (increased) eastbound (westbound) journey times. However, these earlier works all rely on a class of flight planning algorithms (based on the so‐called 'shooting method') that are fundamentally different to those typically used by the aviation industry (based on the so‐called 'network method'). To help resolve any ambiguities associated with these differences and better align both the academic and operational communities, we have therefore conducted an evaluation of methods of estimating time‐optimal flight routes to relate the result obtained from the shooting method with the result obtained from the network method, using identical data from an ensemble of eight different climate models included within the CMIP5 project. Our findings suggest that, contrary to the existing literature, journey times will likely be shorter, irrespective of direction of travel and season, although the magnitude of this difference is negligible given the typical duration of transatlantic flights. Trajectory prediction (TP) results are largely dependent on the characteristics of the ensemble, their method of generation, the projected climate scenario and the temporal periods considered. Importantly, we conclude the choice of TP models is not a crucial factor in assessing the impact of climate change on flight durations—as long as this sample size (i.e., number of trajectories used in deriving statistics) is large. [ABSTRACT FROM AUTHOR]

Herpetospermum darjeelingense (C.B.Clarke) H. Schaef. & S.S. Renner is a rare cucurbit found in Darjeeling, Himalaya. It is known for its use as food and medicine with possible pharmaceutical applications. Here we assess the current and future habitat suitability of H. darjeelingense in the study area using MaxEnt modeling. In order to obtain accurate results for future models, the ensemble method was used. The current suitable habitat covers only 13% of the study area, while the future models for 2050 and 2070 show zero habitat suitability for the species. This strongly indicates a possible local extinction of the species indicating a need for rapid and decisive conservation efforts. [ABSTRACT FROM AUTHOR]

Climate change is expected to exacerbate the hydrological cycle globally and have a significant impact on water resources. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report states that observed and projected increases in both temperature and precipitation variability are the main reasons for projected climate change impacts on natural water resources. The examination of meteorological variables of the region, especially when agriculture is rainfall dependent, is very essential to formulate feasible adaptation strategies. As a result, using CORDEX-SA (Coordinated Regional Downscaling Experiment-South Asia) rainfall data (2021 to 2050), trend analysis was used to examine variations in rainfall data in the Kokkarne catchment of the Seetha river basin. Regression analysis was used to identify the season-wise rainfall trend. Annual, Summer, Monsoon, and Winter rainfall have depicted increasing trends with a rate of 2.46, 1.21, 2.77, and 0.009 mm per year respectively. The post-monsoon rainfall has projected a declining trend with a rate of -1.54 mm per year. Hence it is recommended that the designed strategies in the agricultural sector have to take the increasing, decreasing, and erratic nature of the trend of rainfall into consideration. Further considering the use of a Multi-Model Ensemble (MME) is reducing the SD and CV of rainfall data by 862 mm and 48.5% respectively. 87% of annual rainfall is contributed by monsoon season only with a Standard deviation of 424.4 mm and CV of 12%. [ABSTRACT FROM AUTHOR]

A multiparameter ensemble generated by four configurations of the regional model RegCM4 using different cumulus parameterizations and driven by the HadGEM2‐ES global model is used to project changes in the Caribbean wind field, with the focus on the Caribbean Low‐Level Jet (CLLJ). Two scenarios are considered, the RCP4.5 and RCP8.5. The CLLJ shows a strengthening (weakening) during the summer (winter) months compared to present day conditions in both a near future (2020–2049) and far future (2070–2099) time slice, with an eastward and northward expansion (contraction) in the core region. The warmer conditions produce an increase in specific humidity and an intensified moisture flux from the lower to the middle atmosphere in correspondence of the jet intensification. This occurs as a result of an expansion (contraction) of the North Atlantic Subtropical High of about +2 hPa (−2 hPa) into the Caribbean. All the changes are statistically significant at the 95% confidence level with a consensus across all the ensemble members. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are efficient tools for modeling species geographic distribution under climate change scenarios. Due to differences among predictions of these models, their results are combined using consensus methods to form an ensemble model. This paper provides an optimal combination of the common SDMs according to accuracy and correlation to model the climatic suitability of Quercus brantii in the west of Iran and projects it into the years 2050 and 2070. This is done using 1000 samples of the species presence and absence, 4 bioclimatic variables related to temperature and precipitation, and 10 modeling algorithms. An ensemble combination of Global Climate Models (GCMs) and 4 optimistic and pessimistic greenhouse-gas emissions scenarios were utilized to identify the climatically suitable areas in the years 2050 and 2070. These models were combined using three common statistics, including mean, median, and weighted mean. The predictive accuracies of the single-models and the consensus methods were assessed using the area under the curve (AUC) metric that validates the acceptable performance of the 9 out of the 10 models studied. Applying the genetic algorithm, the best combination of the models was selected including 4 algorithms with accuracy and correlation equal 0.95 and 0.30 respectively. The results show that the Random Forest (RF) model causes less error in the ensemble model and also compensates other models' errors more. Projections into the years 2050 and 2070 showed that in both time periods and under all scenarios, changes will occur in the spatial distribution of this species, and the most severe one would be a 55.6% loss under the most pessimistic scenario in 2070. • Modeling climate change effects on Zagros forests by species distribution models. • Using the genetic algorithm to find the optimum combination of models. • Ensemble modeling approach provides more accurate predictions. • Suitable climatic habitats of the species will decrease over time. [ABSTRACT FROM AUTHOR]

Future changes in crop evapotranspiration (ETc) are of interest to water management stakeholders. However, long-term projections are complex and merit further investigation due to uncertainties in climate data, differential responses of crops to climate and elevated atmospheric CO2, and adaptive agricultural management. We conducted factor-control simulation experiments using the process-based CropSyst model and investigated the contribution of each of these factors. Five major irrigated crops in the Columbia Basin Project area of the USA Pacific Northwest were selected as a case study and fifteen general circulation models (GCM) under two representative concentration pathways (RCP) were used as the climate forcing. Results indicated a wide range in ETc change, depending on the time frame, crop type, planting dates, and CO2 assumptions. Under the 2090s RCP8.5 scenario, ETc changes were crop-specific: +14.3% (alfalfa), +8.1% (potato), −5.1% (dry bean), −8.1% (corn), and −12.5% (spring wheat). Future elevated CO2 concentrations decreased ETc for all crops while earlier planting increased ETc for all crops except spring wheat. Changes in reference ET (ETo) only partially explains changes in ETc because crop responses are an important modulating factor; therefore, caution must be exercised in interpreting ETo changes as a proxy for ETc changes. [ABSTRACT FROM AUTHOR]