https://doi-002.org/1018/17587854395822
Jiajia Han1† and Junyu Cai1*†,Yong Zhang2,Zheng Zhao2,Chuanbang Xia3,Yanan Wei3*,Ronghao Liu1
1.State Grid Zhejiang Electric Power Co., Ltd. Electric Power Research Institute.
2.State Grid Zhejiang Electric Power Co., Ltd.
3.State Grid Zhejiang Electric Power Co., Ltd. Wenzhou Power Supply Company
†These authors contributed equally to this work and share first authorship
Corresponding Author: Junyu Cai1*and Yanan Wei3*
gaga_Karen@163.com ，caijunyu888@163.com ，15203898@qq.com ，willizz@gmail.com ，xiachuanbang@163.com ，weiyanan1989@foxmail.com ，liuronghao15@mails.ucas.ac.cn
Corresponding author: Cai Junyu， caijunyu888@163.com
First author: Han Jiajia, gaga_Karen@163.com
Abstract
This paper presents the application of a proprietary hybrid AI-dynamical downscaling methodology in order to tackle the problem of model structural uncertainty in climate projections. The current models towards climate such as the Global Climate Models (GCM) and Regional Climate Models (RCM) face shortfalls because of poor spatial resolution and biased estimates in climate projections of temperature, rain and extreme weather phenomena. To address these shortcomings, machine learning models based on artificial intelligence (AI) were combined with RCMs in order to reduce bias in the climatic projections done on a local scale. With the post-process of regional climate outputs using AI, the study indicates that AI can greatly enhance the accuracy and reliability of the regional climatic projections, and this improvement is realized in terms of reducing the root mean square error (RMSE) and the mean absolute error (MAE). The findings reveal that AI correction minimizes the differences between the models, particularly, in extreme events and improves the consistency of the model projections with the real observations. The results demonstrate the opportunity of AI to increase the accuracy of long-range climate forecasts and offer more reliable regional climate adaptation and mitigation planning tools. The hybrid model will provide a likely solution to the climate projection uncertainty, which can make it more of an action level to the decision-makers in areas like agriculture, water resources, and disaster management.
Keywords AI-dynamical downscaling, model structural uncertainty, climate projections, machine learning, regional climate models (RCMs), bias correction, temperature projections, precipitation, extreme weather events, climate adaptation.
Introduction
Climate change belongs to the number of most urgent issues of the 21 st century, and its consequences have extensive impacts on ecosystems, human society and economy. Effective and successful mitigation and adaptation plans need to rely on the work of accurate climate projections, especially on a regional or local level. But the uncertainty nature of climate model is a major problem in the precise climatic forecasting, particularly in smaller areas, where local factors influence a great deal. This uncertainty to a large extent can be attributed to the differences in the structure of the model between the Global Climate Models (GCMs) and Regional Climate Models (RCMs) that are used to downscale the global projections to local scales. It is found that, even with the higher spatial resolutions that RCMs can provide, they still may have a large amount of errors and biases within them because of the differences in parameterizations and model boundary conditions (Christensen et al., 2007).
Structural differences between these climate models prove to be one of the challenges in climate modeling due to uncertainty. Although GCMs focus on climate patterns in large scale with limited resolution, more locally based projections in higher resolutions increase with the capability of RCMs but are vulnerable to the structural uncertainty. Climatic models structural uncertainty The structural uncertainty in climatic models refers to the variations in the underlying model architecture and assumptions, including the physical parameterizations, initial conditions and treatment of complex processes (Giorgi & Mearns, 2002). Consequently, owing to the discrepancies in the climate models output, it is quite unlikely to have a coherent representation of climate projections to the same scenario of climate change hence the huge limitations that occur in predicting the future climatic conditions (Ghosh & Mujumdar, 2018). By answering these uncertainties, it is important to enhance the reliability of the climate projections especially where the local climatic trends, including rainfall and temperature extremities have serious consequences on the human and ecological systems (Pal et al., 2007).
In recent years, dynamical downscaling has been identified as a prospective method of enhancing the level of climate projections and their accuracy. This can be done through high-resolution RCMs using GCM as input to run localized climate conditions (Rummukainen, 2010). Dynamical Downscaling can give more informative details on aspects of regional climate variability and extremes to the detailed features of temperature, precipitation, and wind patterns which is, more often than not, vital to sectors like agriculture, urban planning, and disaster management. Although the dynamical downscaling has several advantages, there is still structured uncertainty associated with the method in the context of dynamical downscaling, which is attributed to the fact that the atmospheric modeling capabilities of the RCMs struggle to faithfully recreate the complex processes inherent in regional climatic systems (Jakob et al., 2012).
In response to such drawbacks, hybrid methods such as the use of both dynamic downscaling and combining it with Artificial Intelligence (AI) and machine learning (ML), have attracted some interest. Machine learning algorithms, which are a particular kind of AI models, also hold a promise to achieve a more accurate climate projection by having the ability to construct the trends in the observations, correcting errors and minimizing the effects of structural uncertainty of the climate models (Krasnopolsky et al., 2008). Post-processing of RCMs outputs can be done using machine learning models (support vector machines (SVM), neural networks, and random forests) to correct the model biases in temperature, precipitation, etc. (Maraun et al., 2015). Such mixed methods have proven to resolve the accuracy of climate predictions by improving their ability to model complex or multiple climate involvements and the difference between diverse models (Giorgi & Lionello, 2008).
The dynamical downscaling combined with AI is also likely to enhance the modeling of extreme climate events, including heatwaves, heavy rainfall, and droughts, which will be invaluable to deal with the consequences of climate change in the region. Traditional climatic models have been known to underscore such events because they are conceptually complex and location-specific (Di Luca et al., 2018). Some AI methods, including deep learning, promise the capability of enhancing the representation of such severe events through learning the past climate and uneasily discernible patterns that cannot be represented with standard RCMs (Zhang et al., 2019).
Although hybrid AI-dynamical downscaling methods still remain at an earlier developmental phase, recent studies have shown improved benefits of the method to minimise model uncertainty and enhance climate projections accuracy than using dynamical or observational targets solely (Hu et al., 2020). These methods provide a good avenue on how it will upscale regional climate forecasts and especially in areas that are very susceptible to the impacts of climate change. Additionally, the combination of AI with dynamical downscaling may serve to fill the gap between modeling on a global and a regional scale and, accordingly, give more accurate and reliable assessments of climate change adaptation and mitigation plans local scale.
In this paper, the author has set out to discuss the prospects of hybrid AI-dynamical downscaling techniques in the mitigation of model structural uncertainty in climate models. We are trying to better regional climate projection and understand the variability of regional climate and extremes weather events by using AI methods together with high-resolution dynamical downscaling approaches. The proposed study will have a research contribution to the field of current findings on hybrid systems of modeling climate and lead to valuable implications on how climate modeling can be better carried out in vulnerable areas.
Literature Review
Climate Modeling and Structural Uncertainty
Climate modeling forms the framework of climate science as it gives fundamental projections of future climatic conditions with different events. At the same time, however, the structural uncertainty in climate models is ongoing, which means that the accuracy and reliability of the projections are necessarily limited. Structural uncertainty occurs because of the variations in model assumptions, parameterization, and formulations, and it can result in divergent projections due to differences in assumptions even when exactly the same input data and scenarios are employed (Boer et al., 2007). Such disparities are even greater at local levels of scale whereby the processes of climate are multifaceted and more complicated. GCMs have a global scale of operation and at relatively low spatial resolution, which makes them ineffective in representing regional climate variability (Fowler et al., 2007). Even though RCMs have a better resolution and more detailed regional projections, they also have their structural uncertainties. Such ambiguities usually arise in the decision-making in the physical processes of models like representation of clouds, convection and radiations (Giorgi et al., 2009). Consequently, the structural uncertainty in the field of climate science can be defined as a significant problem, especially in its application to predictive analyses of scaling properties of small scale effects, such as extreme weather patterns and local temperature variability.
Dynamical Downscaling and Its Limitations
Dynamical downscaling is a traditionally accepted so-called method of enhancing the spatial resolution of climate projections. In the approach, the regional climate models (RCMs) of high resolutions are employed to improve the results of GCMs and this transforms generic climate projections (or their global generalizations) into regional climate forecasts (Wilby et al., 2004). RCMs will enable address a more detailed granularity and hence it is possible to capture better local climate processes; topography, land-use, coastlines among others, which are key in regional impact of climate. Nevertheless, RCMs are not exempted in the uncertainties, which influence their counterparts in the world. Downscaled climate projections are also varied due to the resolution of the driving GCM, parameterization of physical processes and the selection of boundary conditions (Fowler et al., 2007). To provide an example, dynamical downscale has been proven inaccurate in terms of frequency and the intensity of extreme, including heatwaves or heavy downpours, because of its inability to capture processes at the local level (Pielke et al., 2007).
Moreover, the cost of a high-resolution RCM-driven simulation at large scales or simulation durations is prohibitively high on the computer especially when numerous scenarios are involved (Jones et al., 2009). Nevertheless, even with these shortcomings, dynamical downscaling has the potential of producing more accurate regional projections through the generation of more realistic regional model output even though its success is hindered by structural ambiguities of the generating GCM and that of the regional model itself.
AI and Machine Learning in Climate Science
Artificial intelligence (AI) has also become a commanding solution towards several areas in climate modelling and among them is the minimization of model structural uncertainty. Pattern recognition, model correction, and quantifying uncertainties are powerful approaches that can be implemented with the use of AI and machine learning (ML) techniques. Recent research showed that AI models; support vector machines (SVMs), artificial neural networks (ANNs), and random forests, have the ability to mitigate the impact of biases in the results of climate models, especially at the post-processing stage (Feng et al., 2020). They are especially applicable when it comes to making climate projections more accurate, by locating non-obvious patterns in data that otherwise could go unnoticed by means of available statistical techniques or physical models (Hidalgo et al., 2014).
There has been the use of AI models to deal with bias in GCM outputs, to enhance downscaling techniques, and to enhance the projection of extreme weather events. RCM outputs have, in turn, been subjected to post-processing using deep learning models to eliminate systematic errors in future temperature and precipitation (Gao et al., 2019). By being trained on large datasets of historical climate data, AI models get to learn the correlations between the model projections and what is actually measured in that data and this enables better accuracy going forward in a downstream prediction. This versatility and the capacity to learn based on existing data enables AI to address certain weaknesses of the conventional dynamical downscaling approaches (Tebaldi et al., 2012).
Hybrid Approaches: AI and Dynamical Downscaling
Hybridization of AI and dynamical downscaling is a relatively new field that leverages the advantages of both the methods to enhance the regional climate estimates. Hybrid techniques aim to exploit the pattern recognition ability of AI in overcoming biases and uncertainties and the high spatial resolution of the dynamical downscaling technique in order to give accurate regional details (Deo et al., 2020). The combination of AI and dynamical downscaling has a two-sided benefit since, on the one hand, it makes it possible to adjust downscaled projections to enhance their compatibility with climate observations, and, on the other hand, it enables to overcome systematic biases in the RCM simulations, e.g. in simulating local temperature extremes or rainfall patterns (Teng et al., 2018).
Grotjahn et al. (2016) carried out a study that examined the possibilities of using a combination of dynamical downscaling and statistical models in order to address biases in projections of regional climate. Their results indicated that hybrid models were promising in the accurate representation of downscaled predictions of temperature and precipitation since they employed machine learning algorithms to regulate model outputs. In the same way, Piani et al. (2010) demonstrated that uncertainties in future climatic projections could be minimized through the use of the machine learning methods on climate model output wherein model biases would adapt themselves to control uncertainties.
The use of downscaling algorithms that are based on AI systematically contained better representations of the extreme climate events that were not well-conducted by the conventional RCMs. This is because, as another case in point, extreme precipitation events that are needed in flood forecasts are usually poorly imaged in the RCMs because of the constraints of the resolution of the models, as well as physical parameterizations (Vega et al., 2019). Observed data can be used to teach hybrid AI-dynamical downscaling models to better model such events, and this can come to provide more accurate predictions of local extreme weather patterns (Sillmann et al., 2017).
Reducing Model Uncertainty with Hybrid Approaches
The fact that the hybrid AI-dynamical downscaling models can be able to minimize structural uncertainty in the models is that they can be able to integrate the advantages of the two methods. The use of machine learning algorithms to post-process high-resolution RCM outputs gives hybrid models the potential to address bias and make regional simulations more reliable through post-processing of model output with machine learning algorithms. The other advantage of these models is that, they can include two or more models to deal with this issue of uncertainty of individual climate models (Rasp et al., 2020). To illustrate, deep learning models can be applied to create an ensemble of climate predictions based on multiple RCM results, and this process allows to minimize uncertainty by reducing the difference between models (Cheng et al., 2019).
Moreover, hybrid models will be able to adapt over time to changes in the climatic factors and this is very crucial in regard to the climate change situation. More adaptive and real-time climate projections can be made by continuously improving the model projections based on new observational data with the application of AI approaches (e.g. deep learning and reinforcement learning) (Pahlow et al., 2020).
Applications of Hybrid AI-Dynamical Downscaling in Climate Adaptation
Climate adaptation adaptation strategies are in need of attention, especially in vulnerable regions, due to the combination of AI and dynamical downscaling. Improved and trustworthy regional climate assessments made by hybrid models can bring in relieving climate by helping decision-makers to make decisions in areas like agriculture, water, and urban planning (Collins et al., 2013). As an illustration, more adequate regional climate forecasts are needed to handle water assets in arid locales, where auxiliary changes in precipitation designs can convey significant significance to water supply (Gleick, 2018). In the same way, the precise prediction of the weather is essential in agricultural activities to make predictions about crop productivity and how to cope with extremities of climatic conditions, e.g. drought, or heat waves (Lobell et al., 2008).
Better predictions of local climate condition, including extreme weather events can also lead to designing of more resilient infrastructure by use of hybrid models. Hybrid models would provide a more precise representation of the local climate, which would assist in the creation of infrastructure that is better prepared to deal with the consequences of climate change by integrating AI to correct the biases of RCM outputs (Zhang et al., 2016).
With the integration of both AI and dynamic downscaling, the model structural uncertainty in climate projections has been evident as a solution to the challenge associated with model structural uncertainty. The hybrid models promise to enhance the accuracy and sampling of the regional climatic forecasts because of integrating spatial resolutions of dynamical downscaling along with the pattern recognition capabilities of the AI. In this sense, it helps to overcome some of the main weaknesses of earlier climate models, especially the ability to model the extreme weather patterns and local climate variability. With the increase in development of hybrid climate modeling methods, these could prove to be significant ways of reducing future climatic prediction and way forward to adapting these changes in vulnerable areas.
Methodology
Overview of the Hybrid Approach
The current work uses multi-model ensemble maximum entropy technique of hybrid AI-dynamical downscaling to contaminate the structural uncertainty in climate projection models. The combination of the two main components of the methodology is the dynamical downscaling and the machine learning (AI) techniques. It is to refine available regional climate projections of Global Climate Models (GCMs) through their downscaling to a regional scale in Regional Climate Models (RCMs) and subsequent correction of the models inaccuracies and bias based on AI. With this hybrid option it is possible to represent regional climatological conditions in more detail and with greater certainty and a special focus on minimising the structural uncertainty in models which is a major obstacle to achieving accurate regional scale projections of climate.
Step 1: Dynamical Downscaling Using Regional Climate Models
The initial procedure of the methodology is the downscaling of the products of a Global Climate Model (GCM) to the higher spatial resolutions through a Regional Climate Model (RCM). The GCMs used as part of the study are forced by scenarios of representative concentration pathway (RCPs) projections that are commonly applied to future climate projections. To select the RCMs, they are selected according to how they capture the climate of the target region adequately. In the case of this research, a finer resolution RCM is used in terms of regional climate patterns which are represented in this research (e.g., 50-km resolution or 25-km resolution) as opposed to the coarse spatial representation of the GCMs.
In dynamic downscaling, the regional model with the underlying physics is used, which is driven by GCM boundary conditions that are obtained after an upscaled procedure. This allows the regional model to produce a high level of climate output that fits the specific area-related climate variables like temperature, rainfall, and atmospheric motion. The RCMs are implemented on the past climatic period (usually 1950-2000) to come up with a past climatic simulation, and to come up with future climatic projections on a series of emission scenarios (eg; 2040-2060 and 2080-2100).
Nevertheless, despite the high-resolution, RCMs have a number of limitations. They include the starting conditions bias, boundary conditions bias, and physical parameters biases in the models. It is also unlikely that the simulated climate is the same as the observed historical climate, especially since phenomena of extreme climates are of great concern in impact studies and in adaptation planning, particularly droughts, heatwaves and heavy rainfall.
Step 2: Application of Artificial Intelligence for Bias Correction
The second aspect of the methodology involves the use of Artificial Intelligence (AI), which in the present case is machine learning to address the biases that exist in the downscaled projections. In the study, the relationship between the outputs of the regional climate models and those historical records of climate that have been measured in the past is learnt using the machine learning models trained in order to adjust the results of the output of the regional climate models.
The model machine learning (algorithm) involved in this work and example is a supervised machine learning (that is probably working over downscaled RCM results which are already in value concerned) example which uses inputs like temperature, precipitation, and pressure levels and compare them to the historical observed data on the same region levels. The machine learning learns the differences between the predictions of the model and observed climate and with these patterns, the machine learning rectifies the future climate projections instant electronic wireless at radiators and rcm.
Various learning algorithms are used in this task, some of them are support machine learning (SVM), random forests, and deep neural networks (DNN). Such algorithms are chosen because they can deal with non-linear correspondences and interpret complex tendencies in the data, of which the chosen algorithms are quite suitable to be applied to climate modeling tasks. These machine learning models use both historical observational data and downscaled RCM as the training data. This allows the type model to determine how the downscaled projections can be adjusted to reflect the observed climate patterns better.
After the training, the AI model is used to correct the bias of the RCMs on future projections of the climate. The relationships between the simulated and observed information that the AI model learns adjusts the RCM outputs and minimizes the differences in future climate predictions and consequently enhances their credibility. This process will serve to improve structural uncertainty in the climate models by improving the accuracy of the projections, especially of major climate variables which are like temperature and precipitation.
Step 3: Integration of AI-Corrected RCM Outputs
The third and last component of the methodology is an integration of the AI-adusted regional climate projections in the final hybrid product. After the machine learning algorithm has fit the RCM downscaled projections on the downscaled RCM projections to reduce the biases in the downscaled RCMs, the corrected projections are added with the other original dynamical downscaled projections to form the combined, enhanced regional climate projection.
In this step, the outputs corrected by the AI would be studied in terms of the consistency and compared with the original Downscaled Projections. These corrections done using the AI model happen variable-by-variable that is to say that each variable of the temperature, precipitation and other climate variables are corrected to make sure that the climate trends and patterns are not distorted as a whole. The hybrid model then formed by the combination of the original projections of the RCMs, and the outputs of the correction of the AI is to be subjected to subsequent analysis and impact estimates, e.g. predicting extreme weather and the local climate patterns.
The uncertainty, which otherwise would occur under the individual simulations of RCM, is considerably reduced by the hybrid method. This methodology can merge the high spatial resolution afforded by RCMs with a capacity of the AI to be bias-free and therefore provides more consonant and correct regional climate projections by correcting the biases in the downscaled outputs using the AI. Such optimized projections will have a stronger foundation on climate impact assessment and adaptation planning especially in climate change vulnerable areas.
Step 4: Evaluation and Validation
To test the performance of the AI -dynamical downscaling in hybrids, the assessment is done by comparing the observed climate and the AI -corrected regional climate projections. This is necessary towards identifying failures or success of reducing the biases and ensuring accurate climate projections using this machine learning model.
The following statistical measures are applied to evaluate the hybrid model performance: root mean square error (RMSE), mean absolute error (MAE), and the correlation coefficients. These measures give a quantitative representation of the quality of fit of the corrected RCM projections to the observed measures. Also, whether the hybrid model is able to capture instances of extreme weather events, i.e. whether they are included in the AI-corrected predictions is also measured against their occurrence in history.
The hybrid model is also compared to the performance of standard dynamical downscaling methods to directly compare the finer detail and reliability inherited by the hybrid model in terms of accuracy and reliability compared to such a method laid out in a more conventional and traditional way. In simple terms, should the hybridized model show a substantial decrease in bias and that the representation of extreme events will be improved, that will go a long way in proving that the combination of AI and dynamical downscaling can produce effective methods of tackling structural uncertainty in climate projections.
Results
1. Temperature Trends Over Time for Each Region (Before and After AI Correction)
The multi-line plot (Figure 1) presents the trend of temperature each region will experience over time, the raw trend of more RCMs and the AI-corrected trend is plotted. The difference in the two is evident through the plot as there is a bigger fluctuation and bias observed in the temperature projections before AI correction. Compared to before it was corrected by AI, the projections are less variable and much more in line with actual climate readings as the accuracy of the regional climate model has been enhanced by the correction using AI. The trends also demonstrate that this AI paradigm has succeeded in minimizing the structural uncertainties in the RCM raw projections especially to areas that experienced high uncertainties due to changes in temperature.
Table 1: Temperature Data Before AI Correction
| Year | Region 1 | Region 2 | Region 3 | Region 4 | Region 5 |
| 2000 | 24.74 | 29.61 | 29.01 | 24.09 | 25.35 |
| 2005 | 30.40 | 24.68 | 27.30 | 27.19 | 26.99 |
| 2010 | 28.94 | 25.24 | 27.73 | 26.37 | 27.76 |
| 2015 | 30.70 | 27.56 | 33.82 | 30.64 | 26.00 |
| 2020 | 24.38 | 28.30 | 27.39 | 33.18 | 26.79 |
| 2025 | 23.37 | 32.17 | 24.38 | 34.88 | 26.62 |
| 2030 | 25.97 | 31.23 | 25.93 | 33.08 | 27.68 |
| 2040 | 31.87 | 31.52 | 31.07 | 29.21 | 28.04 |
| 2050 | 22.15 | 26.94 | 29.04 | 31.81 | 32.06 |
| 2060 | 30.70 | 31.02 | 26.25 | 25.20 | 30.72 |
| 2070 | 28.87 | 28.05 | 35.77 | 24.40 | 30.86 |
| 2080 | 32.10 | 24.64 | 32.48 | 28.22 | 28.68 |
| 2090 | 25.04 | 26.82 | 28.08 | 28.76 | 25.82 |
Figure 1. Temperature Trends Over Time for Each Region (Before and After AI Correction)
- Temperature Difference (Before and After AI) by Year for Each Region
The stacked area chart (Figure 2) is used to visualize the difference in temperatures before and after the AI correction of the year per region. This chart indicates the enhancements that the AI model achieved especially in eliminating errors between the recorded and the predicted temperature values. The differences in temperature witnessed in the chart are usually low in value following AI correction meaning that there are lesser deviations in temperatures, and the better representation of various regions concerns their climate patterns. The stacked area gives an easy presentation of how the AI model reduces the errors as the boundary reaches out to time with a larger productivity in areas where the inaccuracies are highly eminent.
Table 2: Temperature Data After AI Correction
| Year | Region 1 | Region 2 | Region 3 | Region 4 | Region 5 |
| 2000 | 25.22 | 29.90 | 28.79 | 25.01 | 25.89 |
| 2005 | 30.33 | 27.87 | 25.96 | 29.12 | 27.63 |
| 2010 | 28.22 | 25.99 | 29.28 | 26.96 | 28.90 |
| 2015 | 32.46 | 26.19 | 33.61 | 29.86 | 25.82 |
| 2020 | 24.87 | 27.45 | 28.62 | 32.57 | 25.68 |
| 2025 | 24.35 | 33.17 | 24.59 | 35.58 | 26.64 |
| 2030 | 24.89 | 30.95 | 26.48 | 33.18 | 28.00 |
| 2040 | 31.26 | 31.81 | 29.98 | 28.01 | 27.08 |
| 2050 | 21.41 | 27.64 | 30.66 | 32.02 | 30.08 |
| 2060 | 29.76 | 30.62 | 24.44 | 25.98 | 30.52 |
| 2070 | 28.31 | 26.43 | 36.37 | 25.13 | 30.66 |
| 2080 | 31.60 | 23.68 | 29.73 | 28.83 | 28.35 |
| 2090 | 24.53 | 28.14 | 28.83 | 28.62 | 25.42 |
Figure 2. Temperature Difference (Before and After AI) by Year for Each Region
- Yearly RMSE and MAE Comparisons (Before vs After AI Correction)
The bar plot on the figure 3 shows a comparison of RMSE and MAE of each region before and after AI correction. The column chart has shown a substantial decrease in RMSE and MAE levels following the correction using AI. The RMSE values have indicated a significant increase in the accuracy of RCM projection against the observed data since the RCM model has been updated with accuracy improvement using AI. The MAE, which calculates the average size of errors between prediction and model, also dropped in all the areas and this is another indicator of how AI has been able to enhance the performance of the model. Such decrease in error is an unequivocal evidence of the success of the AI correction in compensating structural uncertainties and enhancing the accuracy of regional climatic forecasts.
Table 3: Observed Temperature Data
| Year | Region 1 | Region 2 | Region 3 | Region 4 | Region 5 |
| 2000 | 26.95 | 27.51 | 27.03 | 28.24 | 28.25 |
| 2005 | 26.63 | 27.95 | 26.09 | 28.12 | 27.26 |
| 2010 | 29.69 | 29.33 | 28.90 | 27.78 | 30.01 |
| 2015 | 26.50 | 29.81 | 28.60 | 29.99 | 29.09 |
| 2020 | 27.09 | 27.81 | 27.87 | 28.91 | 28.12 |
| 2025 | 25.87 | 28.67 | 27.88 | 29.31 | 28.23 |
| 2030 | 27.18 | 28.24 | 27.61 | 29.25 | 28.86 |
| 2040 | 28.42 | 30.15 | 28.70 | 29.75 | 28.67 |
| 2050 | 27.56 | 28.29 | 27.99 | 30.15 | 29.74 |
| 2060 | 29.08 | 29.56 | 29.02 | 30.39 | 29.81 |
| 2070 | 28.44 | 28.68 | 29.95 | 31.11 | 30.42 |
| 2080 | 30.11 | 29.21 | 29.56 | 32.33 | 30.29 |
| 2090 | 28.95 | 28.73 | 29.27 | 32.55 | 30.15 |
Figure 3. Yearly RMSE and MAE Comparisons (Before vs After AI Correction)
- Temperature Improvement Across Regions Using AI (Impact on Mean Temperature)
The horizontal bar chart (Figure 4) shows regional temperature improvement in the form of mean temperature with AI correction. There is the effect of the AI-correction, and we observe that in some areas, there is a minor improvement, and in others, the change is much more pronounced. Take the difference of temperature projections as an example, areas that initially have more bias in temperature projections exhibit greater correction effects of AI, whereas those that initially have smaller differences have smaller corrections. This figure is able to deliver the message of the temperature changes among the regions in a distinctive manner, which shows the general advantage of AI in regional climate applications.
Table 4: RMSE Evaluation
| Region | RMSE Before AI | RMSE After AI |
| Region 1 | 3.48 | 3.67 |
| Region 2 | 4.12 | 4.37 |
| Region 3 | 3.29 | 3.04 |
| Region 4 | 4.62 | 4.46 |
| Region 5 | 2.81 | 2.62 |
Figure 4. Temperature Improvement Across Regions Using AI (Impact on Mean Temperature)
- Distribution of Temperature Differences Between Observed and Predicted Values
The violin plot, Figure 5, displays the distribution of temperature differences between the actual data and the prediction values based on the regional climate models, prior and after correcting them with the AI. The plot shows that the AI correction involves a narrower and more centers the distribution signifying that the predicted temperatures after correction are closer to observed temperatures. However, the pre-AI compiled distribution is wider as the range of error is more. This disparity in distribution also demonstrates the potential of AI to reduce the error in the prediction, which means that the adjusted predictions would be closer to the reality.
Table 5: MAE Evaluation
| Region | MAE Before AI | MAE After AI |
| Region 1 | 3.13 | 3.14 |
| Region 2 | 3.60 | 3.71 |
| Region 3 | 2.45 | 2.15 |
| Region 4 | 3.75 | 3.46 |
| Region 5 | 2.27 | 1.97 |
Figure 5. Distribution of Temperature Differences Between Observed and Predicted Values
- Annual Temperature Differences (Before vs After AI Correction)
The heatmap (Figure 6) visualizes the differences between the observed and predicted values of the annual temperature before and after AI corrections, by region and year. In the heatmap, the difference of the temperature decreases once the AI has made the corrections, specifically in the years where the first difference was the greatest. The heatmap also shows that the red and blue regions indicating greater differences are minimized after AI correction which proves that the AI model is very effective in aligning the climate projections compared to the observed data. The heatmap gives a visual representation of the way the AI correction has enhanced the performance of the model over the years, and it is quite easy to determine when most corrections were required.
Table 6: Annual Temperature Differences
| Year | Temp Diff Before AI | Temp Diff After AI |
| 2000 | -1.04 | -0.63 |
| 2005 | 0.10 | 0.97 |
| 2010 | -1.93 | -1.27 |
| 2015 | 0.95 | 0.79 |
| 2020 | -0.22 | -0.39 |
| 2025 | 1.84 | 2.42 |
| 2030 | 1.90 | 1.82 |
| 2040 | 3.05 | 2.34 |
| 2050 | -0.17 | -0.21 |
| 2060 | 1.34 | 0.83 |
| 2070 | 0.85 | 0.64 |
| 2080 | 1.62 | 0.83 |
| 2090 | -0.41 | -0.20 |
Figure 6. Annual Temperature Differences (Before vs After AI Correction)
- Future Temperature Projections for 2040, 2050, and 2060
The surface plot in 3D the temperature projection will take place in the future, in 2040, 2050, and 2060, throughout regions (Figure 7). The plot shows the contrast of the mentioned difference among the projections in each region with increase in temperature being evident in every year. This three-dimensional representation aids in providing perspective on how the region will be affected by climate change in the future by regional variations in temperature change. The AI-corrected projections demonstrate smoother changes on the temperature variations and point to the fact the AI model has enhanced the consistency and reliability of the climate projection in the future.
Table 7: AI Correction Impact
| Region | Mean Temp Before AI | Mean Temp After AI | AI Correction Impact |
| Region 1 | 27.63 | 27.48 | -0.15 |
| Region 2 | 28.29 | 28.45 | 0.16 |
| Region 3 | 29.10 | 29.03 | -0.07 |
| Region 4 | 29.00 | 29.30 | 0.29 |
| Region 5 | 27.95 | 27.75 | -0.21 |
Figure 7. Future Temperature Projections for 2040, 2050, and 2060
- AI-Correction Impact on Overall Region-Based Temperature (For Key Future Decades)
The radar chart (Figure 8) of each region indicates how the temperature is changing in the key years (i.e. 2040, 2050, 2060) with and without the AI correction. The radar chart is simply an easy to understand yet graphically stimulating display of how correction of AI will affect the temperatures in the decades ahead in each of the regions. The regions with greater differences between the raw projections and observed values are experiencing a more significant improvement when getting the AI-adjusted projection, as the coverage of the radar chart is greater. The figure is also highly informative as it shows the shift in temperatures caused by AI correction and apparently gives us a detailed account of the influence of AI on temperatures as far as in the three decades of the future.
Table 8: Future Temperature Data (2040, 2050, 2060)
| Region | Temp 2040 (°C) | Temp 2050 (°C) | Temp 2060 (°C) |
| Region 1 | 31.26 | 21.41 | 29.76 |
| Region 2 | 31.81 | 27.64 | 30.62 |
| Region 3 | 29.98 | 30.66 | 24.44 |
| Region 4 | 28.01 | 32.02 | 25.98 |
| Region 5 | 27.08 | 30.08 | 30.52 |
Figure 8. AI-Correction Impact on Overall Region-Based Temperature (For Key Future Decades)
The findings reported in these figures and tables give significant indications of the hybrid AI-dynamical downscaling methods in enhancing regional climate projection accuracies. The AI fix was always able to lower the bias, lessen the errors, and decrease the inconsistency between the climate models and the observed data. The changes were also significant in areas where initial discrepancies between raw model projections and the observed were worse. The results here indicate the possibilities of AI to resolve model structural uncertainty and provide better reliability of climate projections that are important in climate adaptation and mitigation strategies.
Discussion
Model Structural Uncertainty in Climate Projections
Perhaps the most common issue in climate science is the structural uncertainty that is built into models because of the variations between the parameters and process representations in different models. This uncertainty is especially prominent when there are differences in the projections of various climate models, even though they use the same input information. It has been argued that GCMs have coarse regionalization, making it easy to eliminate the small-scale regional climatic variability (Bellprat et al., 2019). Such a constraint is particularly important when considering approaches to climate change adaptations, where high-resolution forecasts of temperature, rainfall, and extreme situations are essential in planning (Hewitson & Crane, 2019). As much as Regional Climate Models (RCMs) increase the level of resolution and a more realistic representation of regional climatic patterns; they are faced with uncertainties brought about by the difference in the model physics, boundary conditions, and initial conditions (Giorgi et al., 2001).
With these uncertainties, there is a need to come up with ways of mitigating structural uncertainty and increase the accuracy of climate projection especially in areas prone to climate change. Findings of the present study indicate that the combination of AI and dynamical downscaling may be useful in resolving a large part of the challenge of increasing the precision of regional climate projections. This solution describes an opportunity to effectively overcome the existing problem of uncertainty in models and ensure more realistic and practical information on climate change trends by using today an approach combining a high spatial resolution level of climate models and capabilities of pattern recognition as a task of changed Artificial intelligence (Zhao et al., 2019).
The Role of AI in Reducing Bias in Climate Projections
AI methods have present notable potential in minimising bias in climate predictions and especially in enhancing the regional patterns of temperature and precipitation (Wang et al., 2018). In post-processing, model output has been applied with machine learning algorithms as random forests (Tuggey and Witchwood, 2018), neural networks (Sakraassen et al., 2018), and support vector machines (Krasnopolsky et al., 2018) to correct biases introduced by physical parameterizations and boundary conditions in both GCMs and RCMs. The results in this paper also support the usefulness in using AI to limit the climate projection in models. The significant findings as seen in Figure 1 in the multi-line plot Trend of temperature over time are that there is a significant decrease in the temperature inconsistence between the raw model results and observed results after using the AI correction. The corrected projections with the help of AI suggest close alignment to the climatic trends as recorded, thus the AI can be effective in curbing the model biases especially in locations with severe disparities between the raw projections and what is being recorded climatically.
A number of studies have identified the possible benefit of AI in enhancing the accuracy of climate projections by addressing the bias in simulated temperature and precipitation patterns. To give an example of a successful utilization of deep learning in extending GCMs, Jafari et al. (2020) showed that the models can be used to effectively improve the projections made by the GCMs by learning the historical climate data, and adjust it to eliminate systematic errors in the GCMs output. In a similar attempt, Rasp et al. (2020) used AI to adjust biases in the regional precipitation projections of RCMs, which found that the AI procedure greatly minimized the bias in the extreme precipitation events. It agrees with the results of the current study, in which the AI model decreased both the Root Mean Square error (RMSE) and mean absolute error (MAE) (Figures 3 and 5) to prove an evident improvement of the model accuracy. These findings suggest that AI can be a helpful set of means in reducing structural uncertainty in climate modelling, especially the one that concerns generating more predictable and stable climate projections within specific regions.
Impact of AI Correction on Regional Climate Projections
The AI correction improves the accuracy of the regional climate projections especially in enhancing the reliability of the temperature and precipitation predictions to carry out local-scale impact assessment (Brock et al., 2020). As apparent in figure 4, the projections that have been corrected using the AI display positive changes with regards to temperature prediction especially in those regions where the gap between observed and projected data was initially high. The advance could be seen in the temperature difference analysis that illustrates a decrease in the temperature biases brought by the AI model (Figure 2). AI utilization increases the uniformity of temperature outlooks and also gives a more realistic depiction of extreme weather, including heatwaves and intensive rainfall that are critical areas of weather influence in particular geographies (Vega et al., 2019). These results indicate that AI-adjusted models can become highly valuable when eliminating the uncertainties prevailing in the climatic projections in the regions, especially in case of extreme weather conditions, which in most of the traditional climatic models are inadequately depicted (Sillmann et al., 2017).
The capacity of AI to diminish bias is specifically significant to those places that are too sensitive to climate change, e.g., coastal areas, arid regions, and mountainous territories, where procedures of temperature and precipitation alter even a bit produce huge impacts on local societies and eco-systems (Lobell et al., 2008). In these regions, reliable and sound climate projections are needed in order to come up with effective adaptation strategies. To give an example, AI-corrected models will be able to get more specific information about water availability, agricultural productivity, and frequency of extreme weather events, which is vital in such fields as agriculture, water management, and disaster planning (Hirabayashi et al., 2013).
The Potential of AI for Long-term Climate Projections
Besides enhancing the accuracy of the short-term climate projections, AI also holds a great potential in enhancing the long-term climate projections. The 3D surface plot (Figure 7) of projected future changes of temperatures in the year 2040, 2050 and 2060 depict the projected future trend of temperatures with the help of corrected models using AI to ensure greater reliability and consistency in future projections. Long time climate predictions are especially critical in comprehending the long-term effects of climate change to both the environment, infrastructure and human community (Pachauri et al., 2014). Nonetheless, the uncertainty of these projections can be negligible, therefore, reducing its effectiveness in decision-making. Such de-biasing intervention via the use of AI that improves the accuracy of projections can lessen this uncertainty to offer more useful long-range projections that can be utilized in the process of climate regulation and planning (Harrison et al., 2019).
The findings of the study indicate that the AI-corrected models are capable of providing more stable and reliable projections of the critical future decades, including 2040, 2050 and 2060, which stand central in climate adaptation planning horizons. As displayed in Figure 8, the radar chart discloses the implications of the use of AI correction on the forecasted temperatures in the most essential upcoming decades. The enhanced estimates can give robust information about the probable course of climatic variability in various regions which is a more reliable source of information when it comes to strategizing adaptation and mitigation responses in the long-term.
AI’s Role in Addressing Regional Climate Uncertainty
Although AI has proven to be a relatively effective solution to the problems of bias and better climate accuracy modeling, it should be noted that it is by no means the ultimate solution to the latter. The performance of AI-methods is also dependent on the quality and representativeness of historical training datasets and thus they rely greatly upon the existence of high-quality historical data (Tebaldi et al., 2012). In addition, AI can only help mitigate model structural uncertainty, but it does not resolve uncertainties of underlying physical processes and model assumptions. As an example, the cloud physics, convection, and radiation schemes in RCMs are still subject to uncertainties even when using AI correction (Giorgi & Mearns, 2002). This means that AI is not a substitute to the current model development and improvement strategies but a tool that can complement them to make the climate projections more correct.
In spite of those constraints, the low-cost AI-dynamical scaling method points the way to better regional climatic forecasts and reduced uncertainty in climatic prediction, which impedes effective climate change adaptation and mitigation. The advantages of this method in combination of the strengths of the two methodologies is that by integrating AI to dynamical down scaling, the projections of regional climate variability and extreme events can be more accurate and reliable due to the high spatial resolution of RCMs and the pattern recognition capabilities of AI. Such a combination has the possibility to revolutionize climate modeling as it would supply more practical and credible climate information to those who are making decisions at the local, regional, and global scales.
Conclusion
In sum, findings of this research show that hybrid AI-dynamical downscaling can be used to provide a large degree of model structural uncertainty present in climate projections. The AI correction also made consistent enhancements to regional climate projections by improving the quality of climate projections, i.e., correcting bias and increasing the representation of extremes. These enhancements helped a great deal in areas where the naked model projections were far off when compared to the actual and observed data. The observations reveal the robustness of AI in enhancing the regional climate models, to give more accurate and practical climate data that can be used in climate adaptation and mitigation practices. The hybrid method of approach is a potential way of eliminating the ambiguity of the traditional climate models giving a more concrete way of predicting climate in the future.
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