Role Of Climate Models Research Articles

Sea Ice Extent Prediction with Machine Learning Methods and Subregional Analysis in the Arctic

The decline of sea ice in the Arctic region is a critical indicator of rapid global warming and can also influence the feedback processes in the Arctic, so the prediction of sea ice extent and thickness plays an important role in climate modeling and prediction. This paper uses machine learning methods to predict the sea ice extent, and by adjusting the methods and factors, which include the climate variables, the past sea ice extent, and the simple linear-regression-simulated sea ice extent, then we found the best combination to give the result with the highest R2 score. We noticed that with longer periods of past sea ice extent data and shorter periods of climate data, the results appeared to be better. This might be related to the difference in climate and ocean memory. The sub-region sea ice extent prediction shows that the regions with whole-year ice cover are easier to predict and that those regions with sudden weather changes and significant seasonal variability appear to have lower R2 scores in the sea ice extent prediction.

Heterogeneous acceleration algorithms for shallow cumulus convection scheme over GPU clusters

The physical process of atmospheric cumulus convection plays a crucial role in climate modeling, and its complex computational process severely restricts the development of high-resolution climate models. Accelerating the cumulus convective process calculation in climate models is a significant challenge. Traditional CPU-accelerated computing is increasingly unable to meet the growing demand for computing resources from high-resolution climate models. Therefore, developing an efficient cumulus convection scheme is quite valuable and necessary. In response to this demand, this paper selects the University of Washington shallow cumulus (UWshcu) model as the research object and proposes its parallel algorithms suitable for large-scale, heterogeneous, high-performance computing systems: (1) the single GPU acceleration algorithm based on CUDA C, namely GPU-UWshcu; (2) the multi-NVIDIA GPUs acceleration algorithm based on the MPI+CUDA hybrid programming model, namely CGPUs-UWshcu; (3) the multi-AMD GPUs acceleration algorithm based on MPI+HIP, namely HGPUs-UWshcu; (4) the multiple CPUs+GPUs acceleration algorithm based on MPI+OpenMP+HIP, namely MOH-UWshcu. Experimental results show that these algorithms are efficient and have good scalability. GPU-UWshcu achieves a speedup of 74.39× on a single Tesla V100 GPU compared with the serial algorithm running on an Intel Xeon E5-2680 v2 CPU core, and CGPUs-UWshcu achieves a 151.22× speedup on 16 Tesla V100 GPUs compared to a single Intel Xeon E5-2630 v4 CPU (10 cores). On the ORISE supercomputer, HGPUs-UWshcu uses 1024 AMD GPUs to achieve a 664.65× speedup compared to a single CPU (32 cores), with a parallel efficiency of 68.91% compared to using 32 GPUs. Compared to using the same number of CPU cores, MOH-UWshcu uses 8192 CPU cores+1024 GPUs to achieve a speedup of 4.98×, with 55.22 TFLOPS in double precision.

Climatology of Planetary Boundary Layer Height-Controlling Meteorological Parameters Over the Korean Peninsula

Planetary boundary layer (PBL) height plays a significant role in climate modeling, weather forecasting, air quality prediction, and pollution transport processes. This study examined the climatology of PBL-associated meteorological parameters over the Korean peninsula and surrounding sea using data from the ERA5 dataset produced by the European Centre for Medium-range Weather Forecasts (ECMWF). The data covered the period from 2008 to 2017. The bulk Richardson number methodology was used to determine the PBL height (PBLH). The PBLH obtained from the ERA5 data agreed well with that derived from sounding and Global Positioning System Radio Occultation datasets. Significant diurnal and seasonal variability in PBLH was observed. The PBLH increases from morning to late afternoon, decreases in the evening, and is lowest at night. It is high in the summer, lower in spring and autumn, and lowest in winter. The variability of the PBLH with respect to temperature, relative humidity, surface pressure, wind speed, lower tropospheric stability, soil moisture, and surface fluxes was also examined. The growth of the PBLH was high in the spring and in southern regions due to the low soil moisture content of the surface. A high PBLH pattern is evident in high-elevation regions. Increasing trends of the surface temperature and accordingly PBLH were observed from 2008 to 2017.

Ground-Based Cloud Detection Using Graph Model Built Upon Superpixels

Cloud detection plays an important role in climate models, climate predictions, and meteorological services. Although researchers have given increasing efforts on cloud detection, the performance is still unsatisfactory due to the diverse nature of clouds. Considering the fact that one source of information (color or texture) is not enough to segment cloud from clear sky, in this letter, we propose a novel ground-based cloud detection method using graph model (GM) built upon superpixels to integrate multiple sources of information. First, we use the superpixel segmentation to divide the image into a series of subregions according to the color similarity and spatial continuity. Next, adjacent superpixels are merged according to their similarity of extracted features. Finally, we build a GM on the merged superpixels by considering each superpixel as a node and adding edges between neighboring ones. The unary cost is set according to the classification score of Random Forests, while pairwise cost reflects the penalties for color and texture discontinuity between neighboring components. The final segmentation could be acquired by minimizing the cost function. Moreover, the algorithm is computationally efficient as we use the superpixels rather than raw pixels as computation units. Experimental results demonstrate the effectiveness and efficiency of the proposed method for cloud detection.

The role of climate models in adaptation decision-making: the case of the UK climate projections 2009

When attendant to the agency of models and the general context in which they perform, climate models can be seen as instrumental policy tools that may be evaluated in terms of their adequacy for purpose. In contrast, when analysed independently of their real-world usage for informing decision-making, the tendency can be to prioritise their representative role rather than their instrumental role. This paper takes as a case study the development of the UK Climate Projections 2009 in relation to its probabilistic treatment of uncertainties and the implications of this approach for adaptation decision-making. It is considered that the move towards ensemble-based probabilistic climate projections has the benefit of encouraging organisations to reshape their adaptation strategies and decisions towards a risk-based approach, where they are confronted definitively with climate modelling uncertainties and drawn towards a more nuanced understanding of how climate impacts could affect their operations. This is further illustrated through the example of the built environment sector, where it can be seen that the probabilistic approach may be of limited salience for the urban heat island in the absence of a corresponding effort towards a more place-based analysis of climate vulnerabilities. Therefore, further assessment of the adequacy-for-purpose of climate models might also consider the usability of climate projections at the urban scale.

The regional hydrologic impacts of global climate change: the role of climate models

The requirement to increase understanding of the complex interaction between society and the environment is well documented. Dramatic evidence of the vulnerability of anthropogenic systems to short-term weather fluctuations abounds. Taking an historical perspective provides an equally impressive picture of the potential upheaval caused by longer term climate changes. However, the past (and present) may not provide an adequate analogue for the future. The greenhouse theory of climate change suggests that the changes in climate regime to be expected from enhanced atmospheric CO2 will be of similar magnitude to the glacial-interglacial mean temperature difference, but will occur in a fraction of the time. Consequently, considerable emphasis is being placed on the role of physical climate models in determining projections of future global and regional temperature and precipitation patterns. The latter climate changes will have important implications for the distribution (in time and space) of water, a principal natural resource and basic requirement for a variety of human activities. Consequently, climate models are being applied to the question of determining the regional hydrologie response to global climate change. The latter objective is a prerequisite to assessing the likely impacts on the water resources sector. This paper reviews current progress in achieving this aim and outlines some future research directions.

The regional hydrologic impacts of global climate change: the role of climate models

The requirement to increase understanding of the complex interaction between society and the environment is well documented. Dramatic evidence of the vulnerability of anthropogenic systems to short-term weather fluctuations abounds. Taking an historical perspective provides an equally impressive picture of the potential upheaval caused by longer term climate changes. However, the past (and present) may not provide an adequate analoque for the future. The greenhouse theory of climate change suggests that the changes in climate regime to be expected from enhanced atmospheric CO 2 will be of similar magnitude to the glacial-interglacial mean temperature difference, but will occur in a fraction of the time. Consequently, considerable emphasis is being placed on the role of physical climate models in determining projections of future global and regional temperature and precipitation patterns. The latter climate changes will have important implications for the distribution (in time and space) of water, a principal natural resource and basic requirement for a variety of human activities. Consequently, climate models are being applied to the question of determining the regional hydrologic response to global climate change. The latter objective is a prerequisite to assessing the likely impacts on the water resources sector. This paper reviews current progress in achieving this aim and outlines some future research directions.