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The Chinese Academy of Sciences Coupling Interface Generator (CAS‐CIG) is designed to address the complexities of the development and coupling of different component models in Earth System Models based on the Coupler 7 of the Community Earth System Model (CESM). Its application in the Chinese Academy of Sciences Earth System Model (CAS‐ESM) is desc...
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... Indeed, OMPI (Ocean Model Intercomparison Project) is one of the endorsed model intercomparison projects. LICOM2 is employed as the ocean component in the CAS earth system model version 2 [32,33]. Recently, Hailong Liu et al. developed a global eddy-resolution ocean forecasting system LFS (LICOM Forecast System) based on LICOM3 (0.1 • ) [34]. ...
The ocean general circulation model (OGCM) is an essential tool for researching oceanography and atmospheric science. The LASG/IAP climate system ocean model version 3 (LICOM3) is a parallel version of the OGCM. Our goal is to implement and optimize a GPU version of LICOM3 based on compute unified device architecture (CUDA) called LICOM3-CUDA. Considering the characteristics of LICOM3 and CUDA, we design and implement some pivotal optimization methods, including redesigning the numerical algorithms of complicated functions, decoupling data dependency, avoiding memory write conflicts, and optimizing communication. In this paper, we selected two experiments, including 1\(^{\circ }\) (small-scale) and 0.1\(^{\circ }\) (large-scale) resolutions to evaluate the performance of LICOM3-CUDA. Under the experimental environment of two Intel Xeon Gold 6148 CPUs and four NVIDIA Quadro GV100s, the LICOM3-CUDA (1\(^{\circ }\)) achieves a simulation speed of 114.3 simulation-year-per-day (SYPD). Compare with the performance of LICOM3, the LICOM3-CUDA can run much faster with 6.5 times, and the compute-intensive module achieves over 70\(\times\) speedup. In addition, the energy consumption for the simulation year is reduced by 41.3%. As for high-resolution and large-scale simulation, the number of GPUs increased from 96 to 1536 as well as the LICOM3-CUDA (0.1\(^{\circ }\)) time consumption decreased from 3261 to 720 seconds with approximately 4.5\(\times\) of speedup.
Navaid lighting system is an important visual navaid to ensure the flight safety of aircraft, and plays a key role in the process of aircraft approach and landing. The normal operation of the navaid lighting system is directly related to the safety of aircraft takeoff and landing, so it must always work normally. With the rapid development of civil aviation industry, the original manual inspection method of navaid lighting can not meet the requirements. It has become the basic requirement of the navaid lighting system to automatically monitor the navaid lighting equipment and improve the reliability of the navaid lighting system. The design and research of airfield navaid lighting monitoring system based on data mining algorithm is a project involving the design, development and implementation of airfield navaid lighting monitoring system. For the sake of safety, NAV light monitoring system will be designed to monitor all lights in the airport. It can also detect any abnormal light patterns or light pattern changes, which may indicate a possible problem with one or more navigation lights. This helps the airport authorities to take immediate action before any problem occurs with one or more navigation lights, thus preventing accidents. Design includes designing a software.KeywordsAirfield navaid lightingData mining algorithmmonitoring system
Earth system models (ESMs) are common tools to project climate change. The main focus of this thesis is the analysis of climate projections from ESMs participating in the Coupled Model Intercomparison Project (CMIP) with the aim to reduce uncertainties in climate projections with observations. In a first step, climate sensitivity is evaluated in CMIP6 models. For the effective climate sensitivity (ECS), a multi-model range of 1.8-5.6 K is found. This range is higher than in any previous CMIP ensemble before. Possible reasons for this are changes in cloud parameterizations. To reduce uncertainties in the ECS of the CMIP6 models, 11 published emergent constraints on ECS are analyzed. Emergent constraints are approaches to reduce uncertainties in climate projections by combining observations and ESM output. The application of the emergent constraints to CMIP6 data shows a decrease in the skill of the emergent relationships. This is likely related to the increased multi-model spread of ECS in CMIP6, but may in some cases also be due to spurious statistical relationships. The results support previous studies concluding that emergent constraints should be based on independently verifiable physical mechanisms. To overcome these issues of emergent constraints, an alternative approach based on machine learning (ML) is introduced. As target variable, gross primary production (GPP) is studied. In a first step, an existing emergent constraint is used to constrain the global mean GPP at the end of the 21st century in Representative Concentration Pathway (RCP) 8.5 simulations with CMIP5 ESMs to (171 ± 12) GtC yr-1. In a second step, an ML model is used to constrain gridded future absolute GPP. For this, observational data is fed into the ML algorithm that has been trained on CMIP5 data to learn relationships between present-day physically relevant diagnostics and the target variable. In a perfect model setup, the ML model shows superior performance.
An important metric for temperature projections is the equilibrium climate sensitivity (ECS), which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which are the major source of spread in ECS among current models. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here have been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a decrease in skill and statistical significance of the emergent relationship for nearly all constraints, with this decrease being large in many cases. Consequently, the size of the constrained ECS ranges (66 % confidence
intervals) widens by 51 % on average in CMIP6 compared to CMIP5. This is
likely because of changes in the representation of cloud processes from
CMIP5 to CMIP6, but may in some cases also be due to spurious statistical
relationships or a too small number of models in the ensemble that the emergent
constraint was originally derived from. The emergently- constrained best
estimates of ECS also increased from CMIP5 to CMIP6 by 12 % on average.
This can be at least partly explained by the increased number of high-ECS
(above 4.5 K) models in CMIP6 without a corresponding change in the constraint predictors, suggesting the emergence of new feedback processes
rather than changes in strength of those previously dominant. Our results
support previous studies concluding that emergent constraints should be
based on an independently verifiable physical mechanism, and that process-based emergent constraints on ECS should rather be thought of as
constraints for the process or feedback they are actually targeting.
