Systematic biases in U.S. summer integrations with the Center for Ocean‐Land‐Atmosphere Studies (COLA) atmospheric general circulation model (GCM) have been identified and analyzed. Positive surface air temperature biases of 2°–4°K occurred over the central United States. The temperature biases were coincident with the agricultural region of the central United States, where negative precipitation biases also occurred. The biases developed in June and became very significant during July and August. The impact of the crop area vegetation and soil properties on the biases was investigated in a series of numerical experiments. The biases were largely caused by the erroneous prescription of crop vegetation phenology in the surface model of the GCM. The prescribed crop soil properties also contributed to the biases. On the basis of these results the crop model has been improved and the systematic errors in the U.S. summer simulations have been reduced. The numerical experiments also revealed that land surface effects on the atmospheric variables at and near the surface during the North American summer are very pronounced and persistent but are largely limited to the area of the anomalous land surface forcing. In this regard, the midlatitude land surface effects described here are similar to those previously found for tropical regions.

A set of Markov models is developed based on a statistical linearization of 5 coupled ocean‐atmosphere general circulation models used in the Intergovernmental Panel on Climate Changes Fourth Assessment Report (IPCC AR4), and is applied to ensemble prediction of the tropical Indo‐Pacific sea surface temperature variations. By taking advantage of the long data records of IPCC simulations, the linear model is statistically robust, and exhibits a level of ENSO prediction skill comparable to other forecast models. More importantly, the model shows much higher skill in the western Pacific and the tropical Indian Ocean than previously achieved, thus providing new insight and optimism for the predictability of the short‐term climate change in the whole tropical Indo‐Pacific region.

Introduction Climate change isone of the major challenges facing the world today, causing frequent extreme weather events that significantly impact human production, life, and the ecological environment. Traditional climate prediction models largely rely on the simulation of physical processes. While they have achieved some success, these models still face issues such as complexity, high computational cost, and insufficient handling of multivariable nonlinear relationships. Methods In light of this, this paper proposes a hybrid deep learning model based on Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve the accuracy of climate predictions. Firstly, the Transformer model is introduced to capture the complex patterns in cimate data time series through its powerful sequence modeling capabilities. Secondly, CNN is utilized to extract local features and capture short-term changes. Lastly, LSTM is adept at handling long-term dependencies, ensuring the model can remember and utilize information over extended time spans. Results and Discussion Experiments conducted on temperature data from Guangdong Province in China validate the performance of the proposed model. Compared to four different climate prediction decomposition methods, the proposed hybrid model with the Transformer method performs the best. The resuts also show that the Transformer-CNN-LSTM hybrid model outperforms other hybrid models on five evaluation metrics, indicating that the Methods: In light of this, this paper proposes a hybrid deep learning model based on Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve the accuracy of climate predictions. Firstly, the Transformer model is introduced to capture the complex patterns in cimate data time series through its powerful sequence modeling capabilities. Secondly, CNN is utilized to extract local features and capture short-term changes. Lastly, LSTM is adept at handling long-term dependencies, ensuring the model can remember and utilize information over extended time spans. used Support Vector Machines (SVM) as a basic model for climate prediction Pande et al. (2023) . Weirich et al. achieved high accuracy using Random Forest models Weirich-Benet et al. (2023) . Compared to machine learning methods, deep learning methods can analyze deep and complex nonlinear relationships through hierarchical and distributed feature representation Bauer et al. (2023) . Several deep learning methods are widely used for climate prediction, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Among these deep learning methods, climate prediction often involves using LSTM and GRU models because they can address long-term dependencies and prevent gradient explosion issues.As climate science evolves and extreme weather events become more frequent due to climate change, the complexity of accurate climate prediction has intensified. Researchers have increasingly turned to hybrid models, which combine multiple machine learning approaches, to enhance prediction accuracy. These hybrid models consistently outperform traditional machine learning and deep learning models like SVM and LSTM Peng and Ni (2020) . Additionally, optimizing hyperparameters can further refine predictions, leading to more accurate and reliable climate forecasting. This paper adopts the Transformer to process raw climate sequences and proposes a new hybrid method for climate prediction. By combining the strengths of each component, this method aims to improve accuracy and capture long-term dependencies in the data. The key innovation of our work is the application of the Transformer to data decomposition, enhancing the model’s performance by effectively decomposing complex time series data into its underlying components. (2023) proposed a new combined prediction model for predicting precipitable water vapor (PWV). This model combines Variational Mode Decomposition (VMD) and the Multi-Objective Harris Hawks Optimization (MOHHO) algorithm, integrating four nonlinear models and two linear models. Through effective data preprocessing and weight optimization, the model significantly improved the accuracy and stability of PWV predictions, providing technical support for selecting the timing of artificial rainfall operations. Vo et al. (2023) proposed a hybrid model based on Long Short-Term Memory networks and climate models (LSTM-CM) for drought prediction. They developed a comprehensive flood risk map considering nine influencing factors, assigned weights to each factor using the Analytic Hierarchy Process (AHP), and integrated GIS with geospatial data to generate the flood risk map. This method performed excellently in flood risk modeling. Chin and Lloyd (2024) proposed a hybrid model based on autoregressive Long Short-Term Memory networks (LSTM) for climate change prediction. The study used the ensemble mean version of the ERA5 dataset to develop a baseline machine learning model capable of predicting the overall long-term trends of Earth’s climate and weather, accurately capturing seasonal patterns. Predicting climate change using an autoregressive long short-term memory model . Front. Environ. Sci. 12 , 1301343 . 10.3389/fenvs.2024.1301343 CrossRef Google Scholar View reference in article 9 Fahad S. Su F. Khan S. U. Naeem M. R. Wei K. ( 2023 ). Implementing a novel deep learning technique for rainfall forecasting via climatic variables: an approach via hierarchical clustering analysis . Sci. Total Environ. 854 , 158760 . 10.1016/j.scitotenv.2022.158760 CrossRef Google Scholar View reference in article 10 Han Y. Sun K. Yan J. Dong C. ( 2023 ). The cnn-gru model with frequency analysis module for sea surface temperature prediction . Soft Comput. 27 , 8711 – 8720 . Systems 11 , 483 . 10.3390/systems11090483 CrossRef Google Scholar View reference in article 14 Liu Y. Li D. Wan

global climate models are used for weather forecasting, understanding the climate, and forecasting climate change. Atmospheric GCMs (AGCMs) model the atmosphere A general circulation model (GCM) is a type of climate model. It employs a mathematical model of the general circulation of a planetary atmosphere or ocean. It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for computer programs used to simulate the Earth's atmosphere or oceans. Atmos Co… In… A general circulation model (GCM) is a type of climate model. It employs a mathematical model of the general circulation of a planetary atmosphere or ocean. It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for computer programs used to simulate the Earth's atmosphere or oceans. Atmospheric and oceanic GCMs (AGCM and OGCM) are key components along with sea ice and land-surface components. GCMs and global climate models are used for weather forecasting, understanding the climate, and forecasting climate change. Atmospheric GCMs (AGCMs) model the atmosphere and impose sea surface temperatures as boundary conditions. Coupled atmosphere-ocean GCMs (AOGCMs, e.g. HadCM3, EdGCM, GFDL CM2.X, ARPEGE-Climat) combine the two models. The first general circulation climate model that combined both oceanic and atmospheric processes was developed in the late 1960s at the NOAA Geophysical Fluid Dynamics Laboratory AOGCMs represent the pinnacle of complexity in climate models and internalise as many processes as possible. However, they are still under development and uncertainties remain. They may be coupled to models of other processes, such as the carbon cycle, so as to better model feedback effects. Such integrated multi-system models are sometimes referred to as either "earth system models" or "global climate models." Versions designed for decade to century time scale climate applications were created by Syukuro Manabe and Kirk Bryan at the Geophysical Fluid Dynamics Laboratory (GFDL) in surface pressure horizontal components of velocity in layers temperature and water vapor in layers radiation, split into solar/short wave and terrestrial/infrared/long wave parameters for: convection land surface processes albedo hydrology cloud cover A GCM contains prognostic equations that are a function of time (typically winds, temperature, moisture, and surface pressure) together with diagnostic equations that are evaluated from them for a specific time period. As an example, pressure at any height can be diagnosed by applying the hydrostatic equation to the predicted surface pressure and the predicted values of temperature between the surface and the height of interest. Pressure is used to compute the pressure gradient force in the time-dependent equation for the winds. OGCMs model the ocean (with fluxes from the atmosphere imposed) and may contain a sea ice model. For example, the standard resolution of HadOM3 is 1.25 degrees in latitude and longitude, with 20 vertical levels, leading to approximately 1,500,000 variables. AOGCMs (e.g. HadCM3, GFDL CM2.X) combine the two submodels. They remove the need to specify fluxes across the interface of the ocean surface. These models are the basis for model predictions of future climate, such as are discussed by the IPCC. AOGCMs internalise as many processes as possible. They have been used to provide predictions at a regional scale. While the simpler models are generally susceptible to analysis and their results are easier to understand, AOGCMs may be nearly as hard to analyse as the climate itself. Coupled AOGCMs use transient climate simulations to project/predict climate changes under various scenarios. These can be idealised scenarios (most commonly, CO2 emissions increasing at 1%/yr) or based on recent history (usually the "IS92a" or more recently the SRES scenarios). Which scenarios are most realistic remains uncertain. The 2001 IPCC Third Assessment Report Figure 9.3 shows the global mean response of 19 different coupled models to an idealised experiment in which emissions increased at 1% per year. Figure 9.5 shows the response of a smaller number of models to more recent trends. For the 7 climate models shown there, the temperature change to 2100 varies from 2 to 4.5 °C with a median of about 3 °C. Future scenarios do not include unknown events – for example, volcanic eruptions or changes in solar forcing. These effects are believed to be small in comparison to greenhouse gas (GHG) forcing in the long term, but large volcanic eruptions, for example, can exert a substantial temporary cooling effect. Human GHG emissions are a model input, although it is possible to include an economic/technological submodel to provide these as well. Atmospheric GHG levels are usually supplied as an input, though it is possible to include a carbon cycle model that reflects vegetation and oceanic processes to calculate such levels. In 1956, Norman Phillips developed a mathematical model that could realistically depict monthly and seasonal patterns in the troposphere. It became the first successful climate model. Following Phillips's work, several groups began working to create GCMs. The first to combine both oceanic and atmospheric processes was developed in the late 1960s at the NOAA Geophysical Fluid Dynamics Laboratory. By the early 1980s, the United States' National Center for Atmospheric Research had developed the Community Atmosphere Model; this model has been continuously refined. In 1996, efforts began to model soil and vegetation types. Later the Hadley Centre for Climate Prediction and Research's HadCM3 model coupled ocean-atmosphere elements. The role of gravity waves was added in the mid-1980s. Gravity waves are required to simulate regional and global scale circulations accurately.