Dynamic System Model to Predict Global Sea-Level Rise and Temperature Change

<div><span>Model-based projections of ice-sheet thresholds and global sea-level rise are severely constrained by </span><span>instrumental observations being only decadal to century-long. As we improve our understanding of these processes, projections just a few years old are now considered conservative, raising concerns about our ability to successfully plan for abrupt future change. </span><span>Past periods of abrupt and extreme warming offer ‘process analogues’ that can provide new insights into the future rate of response of polar ice sheets to warming of the Earth system. The Last Termination </span><span>(20,000-10,000 years ago or 20-10 ka BP) </span>in the North Atlantic region was characterised by a series of abrupt climatic changes including rapid warming at 14.7 ka BP (the start of the “Bølling”, or GI-1 in the Greenland ice-core isotope stratigraphy) which was accompanied by an Antarctic Cold Reversal (ACR) in the south. Potentially important, during the onset of GI-1, warming persisted in the south for some 256±133 calendar years before the ACR, providing a period of time during which both polar regions experienced increasing temperatures. Sometime around the onset of GI-1 and the ACR, Meltwater Pulse 1A (MWP-1A) formed an abrupt sea level rise of ~15 metres, and was coincident with a period of enhanced iceberg flux in the Southern Ocean. It seems likely the majority of the sea level rise came from the Northern Hemisphere – up to 5-6 metres from the Laurentide Ice Sheet – though the timing remains uncertain. The contribution of Antarctic Ice Sheets (AIS) to global mean sea level (GMSL) rise during MWP-1A range from ‘high-end’ scenarios (>10 m contributing over half of the total GMSL rise), to ‘low-end’ (scenarios with little to no contribution). Here we report the results of a multidisciplinary study, with refined age and Antarctic ice-sheet modelling of the MWP-1A sea-level rise. With the recently released international radiocarbon calibration curve (IntCal20), our Bayesian age modelling of terrestrial ages from flooded mangrove swamps suggests global <span>sea level rose across a mean age range of 14.58 ka BP to 14.42 ka BP, with a mean rate of sea-level rise of 0.94 metres per decade (14.97 metres over 160 years). Because the calibrated age range at 95% confidence overlaps in this age model, it is possible the 15 metre rise during MWP1A could have taken place essentially instantaneously. Even the most conservative age modelling we have undertaken indicates an extraordinary rapid rate of sea-level rise; two orders of magnitude larger than the mean rate of global sea level rise since 1993 (0.03±0.003 metres per decade). Our ice-sheet modelling suggests a substantial and rapid loss of Antarctic ice mass (mostly from the Weddell Sea Embayment and the Antarctic Peninsula), synchronous with warming and ice loss in the North Atlantic. The drivers and mechanisms of the observed near-synchronous interhemispheric changes will be discussed, with implications for the future.</span></div>

Sea-level rise enhances carbon accumulation in United States tidal wetlands

Projections of global sea-level rise into the future have become more pessimistic over the past five years or so. A global rise by more than one metre by the year 2100 is now widely accepted as a serious possibility if greenhouse gas emissions continue unabated. That is witnessed by the scientific assessments that were made since the last IPCC report was published in 2007. The Delta Commission of the Dutch government projected up to 1.10 m as a 'high-end' scenario (Vellinga et al 2009). The Scientific Committee on Antarctic Research (SCAR) projected up to 1.40 m (Scientific Committee on Antarctic Research 2009), and the Arctic Monitoring and Assessment Programme (AMAP) gives a range of 0.90–1.60 m in its 2011 report (Arctic Monitoring and Assessment Programme 2011). And recently the US Army Corps of Engineers recommends using a 'low', an 'intermediate' and a 'high' scenario for global sea-level rise when planning civil works programmes, with the high one corresponding to a 1.50 m rise by 2100 (US Army Corps of Engineers 2011).

The rate of sea level rise (SLR) along the Southeast Coast of the U.S. increased significantly after 2010. While anthropogenic radiative forcing causes an acceleration of global mean SLR, regional changes in the rate of SLR are strongly influenced by internal variability. Here we use observations and climate models to show that the rapid increase in the rate of SLR along the U.S. Southeast Coast after 2010 is due in part to multidecadal buoyancy-driven Atlantic meridional overturning circulation (AMOC) variations, along with heat transport convergence from wind-driven ocean circulation changes. We show that an initialized decadal prediction system can provide skillful regional SLR predictions induced by AMOC variations 5 years in advance, while wind-driven sea level variations are predictable 2 years in advance. Our results suggest that the rate of coastal SLR and its associated flooding risk along the U.S. southeastern seaboard are potentially predictable on multiyear timescales.

Sea-level rise (SLR) is magnifying the frequency and severity of extreme sea levels (ESLs) that can cause coastal flooding. The rate and amount of global mean sea-level (GMSL) rise is a function of the trajectory of global mean surface temperature (GMST). Therefore, temperature stabilization targets (e.g. 1.5 °C and 2.0 °C of warming above pre-industrial levels, as from the Paris Agreement) have important implications for coastal flood risk. Here, we assess, in a global network of tide gauges, the differences in the expected frequencies of ESLs between scenarios that stabilize GMST warming at 1.5 °C, 2.0 °C, and 2.5 °C above pre-industrial levels. We employ probabilistic, localized SLR projections and long-term hourly tide gauge records to estimate the expected frequencies of historical and future ESLs for the 21st and 22nd centuries. By 2100, under 1.5 °C, 2.0 °C, and 2.5 °C GMST stabilization, the median GMSL is projected to rise 48 cm (90% probability of 28–82 cm), 56 cm (28–96 cm), and 58 cm (37–93 cm), respectively. As an independent comparison, a semi-empirical sea level model calibrated to temperature and GMSL over the past two millennia estimates median GMSL rise within 7–8 cm of these projections. By 2150, relative to the 2.0 °C scenario and based on median sea level projections, GMST stabilization of 1.5 °C spares the inundation of lands currently home to about 5 million people, including 60 000 individuals currently residing in Small Island Developing States. We quantify projected changes to the expected frequency of historical 10-, 100-, and 500-year ESL events using frequency amplification factors that incorporate uncertainty in both local SLR and historical return periods of ESLs. By 2150, relative to a 2.0 °C scenario, the reduction in the frequency amplification of the historical 100 year ESL event arising from a 1.5 °C GMST stabilization is greatest in the eastern United States, with ESL event frequency amplification being reduced by about half at most tide gauges. In general, smaller reductions are projected for Small Island Developing States.

<p>Wind patterns projected for the region, together with sea level rise and land rise, call into question our current understanding of extreme storm surges in the Danish coastal area. The Danish Meteorological Institute (DMI) will research changes in the extreme statistics of sea level in the twenty-first century through the 'Danish Climate Atlas,' a new national climate service initiative. The study will make use of multi-scenarios, multi-models and multi-parameters approach to focus on the uncertainty of the projected change in extreme statistics of sea level.  Historical sea level records suggest that the relative sea level (RSL) along the Danish North Sea coast south of Skagerrak has been increasing with the global mean sea level (GMSL) rise. However, RSL has been absent in the central Skagerrak-Kattegat Seas, owing to the Fennoscandian post-glacial land-uplift offsetting the GMSL rise. According to the recent IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC), due to Antarctic ice sheet dynamics, GMSL would grow more than previously estimated in the IPCC Fifth Assessment Report (AR5) by the end of the twenty-first century under RCP8.5. We regionalized the SROCC sea level forecasts for the "Danish Climate Atlas" dataset. Our findings indicate that sea level projections under RCP8.5 result in a > 40 cm RSL rise in the Skagerrak-Kattegat Seas by the end of the twenty-first century, which may necessitate a new adaptation strategy in this region. Under the RCP8.5 scenario, the rate of mean sea level rise will exceed the rate of land rise earlier than previously estimated by AR5. We emphasize, in particular, the impact of these new predictions on future severe sea levels in this region. Our findings suggest that this more current GMSL prediction should be factored into coastal risk assessments in the Skagerrak-Kattegat Seas in this century.</p>

<p>    Global warming-related SLR (sea level rise) constitutes a substantial threat to Macau, due to its low elevation, small size and ongoing land reclamation. This study was devised to determine the long-term variation of sea level change in Macau, as well as to develop future projections based on tide gauge and satellite data and GCM simulations, aiming to provide knowledge for SLR mitigation and adaptation.</p><p>    Based on local tide gauge records, sea level in Macau is now rising at an accelerated rate: 1.35 mm yr<sup>−1</sup> over 1925–2010 and jumping to 4.2 mm yr<sup>−1</sup> over 1970–2010, reflecting an apparent acceleration of SLR. Furthermore, the sea level near Macau rose 10% faster than the global mean during the period from 1993 to 2012. In addition, the rate of VLM (vertical land movement) at Macau is estimated at -0.153mm yr<sup>-1</sup>, contributing little to local sea level change.</p><p>    In the future, as projected by a suite of climate models, the rate of SLR in Macau will be about 20% higher than the global average. This is induced primarily by a greater-than-average rate of oceanic thermal expansion in Macau, together with enhanced southerly anomalies that lead to a piling up of sea water. Specifically, the sea level is projected to rise 8–12, 22–51 and 35–118 cm by 2020, 2060 and 2100 with respect to the 1986–2005 baseline climatology, respectively, depending on the emissions scenario and climate sensitivity. If we consider the medium emissions scenario RCP4.5 along with medium climate sensitivity, Macau can expect to experience an SLR of 10, 34 and 65 cm by 2020, 2060 and 2100. If the worst case happens (RCP8.5 plus high climate sensitivity), the SLR will be far higher than that in the medium case; namely, 12, 51 and 118 cm by 2020, 2060, and 2100, respectively. The SLR under the lower emissions scenario is expected to be less severe than that under the higher emissions scenarios: by 2100, an SLR of 65–118 cm in Macau under RCP8.5, almost twice as fast as that under RCP2.6. The key source of uncertainty stems from the emissions scenario and poor knowledge of climate sensitivity. By 2020, the uncertainty range is only 4 cm, yet by 2100 the range will be increased to 83 cm.</p>

Increased sea level rise accelerates carbon sequestration in a macro-tidal salt marsh.

Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems contributes to large uncertainties in predicting long‐term sea level rise impacts. Here we explore and demonstrate the capabilities of a Bayesian network (BN) to predict long‐term shoreline change associated with sea level rise and make quantitative assessments of prediction uncertainty. A BN is used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN is used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates. Results demonstrate that the probability of shoreline retreat increases with higher rates of sea level rise. Where more specific information is included, the probability of shoreline change increases in a number of cases, indicating more confident predictions. A hindcast evaluation of the BN indicates that the network correctly predicts 71% of the cases. Evaluation of the results using Brier skill and log likelihood ratio scores indicates that the network provides shoreline change predictions that are better than the prior probability. Shoreline change outcomes indicating stability (−1 < rate < 1 m/yr) or erosion (rate < −1 m/yr) tend to occur for two sets of input scenarios. Stable shoreline change rates occur mainly for low rates of relative sea level rise and occur in low‐vulnerability geomorphic settings. Rates indicating erosion result for cases where the rate of relative sea level rise is high and moderate‐to‐high vulnerability geomorphic settings occur. In contrast, accretion (rate > 1 m/yr) was not well predicted. We find that BNs can assimilate important factors contributing to coastal change in response to sea level rise and can make quantitative, probabilistic predictions that can be applied to coastal management decisions.

Sea level data from the Chesapeake Bay are used to test a novel new analysis method for studies of sea level rise (SLR). The method, based on Empirical Mode Decomposition and Hilbert‐Huang Transformation, separates the sea level trend from other oscillating modes and reveals how the mean sea level changes over time. Bootstrap calculations test the robustness of the method and provide confidence levels. The analysis shows that rates of SLR have increased from ∼1–3 mm y−1 in the 1930s to ∼4–10 mm y−1 in 2011, an acceleration of ∼0.05–0.10 mm y−2 that is larger than most previous studies, but comparable to recent findings by Sallenger and collaborators. While land subsidence increases SLR rates in the bay relative to global SLR, the acceleration results support Sallenger et al.'s proposition that an additional contribution to SLR from climatic changes in ocean circulation is affecting the region.

In recent decades, climate change has emerged as a significant issue, causing ongoing increases in ocean and atmospheric temperatures. This trend indicates that sea levels are expected to rise at faster rates in the future compared to the present sea level. Ongoing increases in sea levels could potentially trigger catastrophic natural disasters worldwide. So that reason, predicting sea level rise (SLR) is crucial for future planning in areas such as human living conditions, flood prevention, and coastal development. This study focuses on evaluating the ability of conventional and deep learning time series methods such as seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM) neural network, and gated recurrent unit (GRU) in estimating the current and future global mean SLR. The models were trained and tested using monthly SLR data collected between 1993 and 2023 and then future predictions were made until 2050. A total of 366 monthly SLR data were used where 288 SLR data (78%) from January 1993 to December 2016 were utilized in the training phase and 78 SLR data (22%) from January 2017 to June 2023 were utilized in the testing phase. The findings demonstrate that although the SLR values estimated with the all developed models are closely compromised with real SLR values in the testing phase, the LSTM model provides more precise predictions than the others. While MAPE, MAE and RMSE parameters for the prediction of SLR data with the LSTM model are observed as 0.0631%, 0.0058 cm and 0.0073 cm, respectively, they are detected as 0.0899%, 0.0084 cm and 0.0109 cm with GRU model. On the other hand, in the testing stage, the worst estimation is performed with the SARIMA model with a MAPE of 0.1335%, MAE of 0.0123 cm and RMSE of 0.0155 cm. Furthermore, the LSTM algorithm which is observed as the best accurate model, predicts the SLR values as 17.218 cm by July 2040 and 21.236 cm by July 2050 when the global average sea level at the beginning of 1993 is taken as reference. Therefore, the developed algorithms show potential as effective tools for modeling and predicting global mean sea level rise in the future and this study will be highly valuable for decision-makers in creating mitigation strategies for sea level rise associated with climate change, utilizing the developed models.

In an attempt to reduce CO2 emissions and tackle climate change, the UK government has set targets of 10% of energy output by 2010, and 15% by 2015, to be sourced from renewable energy, with an aspirational 20% by 2020 (Morley 2006). This is a considerable challenge, and the race is on to meet these targets. Of the renewables sufficiently advanced to contribute to these goals, wind energy constitutes the major component, with offshore wind farms expected to deliver the bigger share (British Wind Energy Association; http://www.bwea.com). Other sources of renewable energy at different stages of development in the UK are: hydro, which currently supplies 2% of the UK's installed energy capacity (and is particularly important in Scotland); solar; tidal (tidal stream); wave power; and biomass – all of which are relatively small scale at present. Formerly, considerable investment was made into research in tidal power barrages (Clark 2006). Whilst these are no longer at the forefront of renewable energy development in the UK, the associated research has increased our understanding of birds in intertidal habitats; and hence the potential impacts on birds of energy generation schemes in these habitats. Clearly, the impacts of a changing climate – sea level rise, warmer temperatures, changes in precipitation, and increasing extreme weather events, for example – all have implications for birds and other biodiversity (IPCC 2001), so renewable energy is welcomed as one of a raft of measures to help slow climate change. However, renewable energy developments can have their own impacts on biodiversity (e.g. Langston & Pullan 2003, Barrios & Rodriguez 2004, Hötker et al. 2004). What are these impacts? What research has been undertaken into their nature and likelihood? What research is needed? What contribution can ecological research make to inform renewable energy developments, thus minimizing any deleterious effects on biodiversity? In recognition of the need to take stock, improve the mutual understanding of the technical and biological issues arising from different forms of renewable energy, and to attempt to minimize the potential conflict between renewables and birds, the British Ornithologists’ Union (BOU) held its annual spring conference in 2005 on Wind, Fire and Water: Renewable Energy and Birds. The conference was held at the University of Leicester, 1–3 April 2005, supported by The Royal Society for the Protection of Birds; the Department of Trade & Industry (for England and Wales); the Renewables Advisory Board; British Wind Energy Association; and English Nature. There were 190 attendees, comprising a broad range of interests, including: the scientific community; conservation bodies; environmental consultancies; government; and industry. This was an excellent opportunity to stimulate debate between people with different interests and perspectives, with the aim of starting a discourse that would continue beyond the conference. The topic of renewable energy routinely polarizes views, and the purpose of the conference was to: present an objective overview of the technologies and their potential effects on birds; methods of minimizing the risk of adverse impacts; and to identify gaps in knowledge and how to plug them. The first presentation, on Friday evening, set the scene for the rest of the conference, and provided the link with an earlier BOU conference that was dedicated to climate change (Rehfisch et al. 2004). Prof. Brian Huntley (University of Durham), reminded delegates that climate change per se is not new, but the anthropogenic influence this time around is unprecedented (Huntley et al. 2006). Furthermore, the predicted rate and magnitude of change in global temperature and greenhouse gas emissions also are without parallel, at least during the evolution of most extant species. He described how we can learn from past responses to climate change and model likely responses to future climate change, illustrating this with examples from predictive models of changing bird distributions for Europe and Africa. Spatial responses are most likely – for those organisms that can move rapidly enough – and therein lies the problem. For many species, the shift in their preferred climate may outpace their ability to respond. The stark prognosis from a recently published global study by Thomas and colleagues (2004), is that a high proportion of plant and animal taxa (9–32%) might be committed to extinction by 2050. There is a clear impetus for ‘action now’– to slow the pace of, or ideally reduce, future climate disruption – as termed by Dr Mark Avery (RSPB's Director of Conservation); a definition that better reflects the problems associated with climate change predictions. There is an urgent need to reduce emissions of carbon dioxide, reduce dependence on fossil fuels, and use energy resources more wisely, to counter what is increasingly considered by some as the greatest threat to global biodiversity. We need to tackle other causes of accelerated climate change too, for example transport, in particular aviation, but that is a debate for another occasion. There was reference to nuclear power – because it is a carbon-free technology – but it is not renewable, and that thorny issue too was left for another day. Internationally, the political debate is divided in terms of how to combat climate disruption, but there is recognition that a mix of renewable sources of energy has an essential role to play as part of a suite of measures. Following a welcome by the BOU's President, Prof. Chris Perrins, the official opening address was given by Elliot Morley MP, Minister for Environment and Agri-Environment (Morley 2006). Mr Morley stressed the need to maintain reliability, adequacy and affordability of energy supply, and for energy efficiency measures to be adopted in homes, businesses and transport. Of the renewable sources of energy, wind power is currently the most cost-effective, and both onshore and offshore wind farms are expected to be the main contributor to the 2010 target of 10% from renewable sources. This is an ambitious first goal in terms of timescale, and has brought into sharp focus the paucity of information regarding the impacts on biodiversity. The longer-term vision includes bringing online several novel technologies, notably wave and tidal, as well as increased involement from more established renewable sources, such as hydro and biomass, of which the latter is set to boost its contribution considerably. Energy generation, including that from renewable sources, has its own potential for environmental harm. The requirement is to balance the risk of adverse impacts on biodiversity, with the need to address climate disruption. The Minister welcomed the conference as timely and helpful in identifying the issues and considering mitigation, stating that renewables cannot be at the expense of biodiversity. In the lively open-floor discussion that followed the opening speech, the Minister said that, whilst one could not say ‘no, never’, Special Protection Areas (SPAs) should be last on the list for development proposals. He also emphasized the need for monitoring to plug gaps in information and referred to the Marine Bill which will provide for spatial planning of activities in the marine environment, including energy, fishing, and aggregates, thereby helping to minimize conflict. Further lively debate took place during the conference, culminating in the plenary session that closed the conference. Before that, a full and diverse programme of conference presentations took centre stage, as presented in these Proceedings of the 2005 Annual Conference of the BOU. Wind, Fire & Water: Renewable Energy & Birds was a conference organized by Rowena Langston (The RSPB) and Steve Dudley (BOU) on behalf of the British Ornithologists’ Union. The BOU is grateful to the individual speakers and their respective organizations for presenting their work at the conference. The BOU and the conference organizers are grateful to Richard Evans, Guy Anderson, Sam Gardner and Alison McLennan (all The RSPB) for their help and support during the conference weekend. The BOU is very grateful to Rowena Langston (Guest Editor) and her editorial team, Guy Anderson, Allan Drewitt, Richard Evans, David Gibbons, Richard Oxley and Jeremy Wilson, for their invaluable work in delivering these proceedings within 12 months of the conference being held. These proceedings have been published with generous support from The RSPB, Department of Trade and Industry, Renewables Advisory Board, British Wind Energy Association and English Nature.

<p>corresponding author: N.Flokos</p><p>flksnik@gmail.com</p><p>ABSTRACT</p><p>Sea level change is one of the key indicators of climate change with numerous effects such as flooding, erosion of beaches, salt intrusion.  The detailed global picture of sea level and the monitoring of its spatial-temporal changes is performed by Satellite Altimetry (SA). Nowadays, SA data compare well with measurements from the global tide gauge network, but the aim of 0.3 mm/year accuracy in the altimeter derived rate of global mean sea level rise is still not fully met. </p><p>Whilst the precise determination of global and regional sea level rise from SA data is promising, there is however an observational gap in our knowledge regarding the coastal zone. While Tide Gauges (TG) are usually located at the coast, therefore providing coastal sea level measurements, altimeters have difficulties there. Filling this gap becomes important when considering that the impact of sea level rise can be devastating on the coast with effects on society and ecosystems. This makes it even more significant knowing that there are many stretches of the world’s coast that still do not possess in situ level measuring devices.  </p><p>This work aims to discuss the available data and methods that link the SA measurements of sea level rise with TG measurements. Whilst there is rich literature on relevant applications, it is important to have a clear and concise methodology on this.</p><p>Tide gauge data</p><p>Several post processing steps need to be applied to the raw TG data to enrich the raw Sea Surface Heights (SSH) values and make them comparable with SA data. There are several geophysical corrections, such as pressure and wind effects, which can be applied to TG data in order to deduce  Sea Level (SL) and be consistent with altimeter data. High frequency atmospheric effects on TG data are corrected using the Dynamic Atmospheric Correction (DAC) provided by AVISO. One other large uncertainty is the vertical stability of the TG benchmark over time. TG data must be corrected for the Vertical Land Motion (VLM) to enable the comparison of two sea level measurements (TG and SA) and their later integration within the surfaces of the absolute sea heights. The main VLM dataset can be obtained from SONEL database (SONEL 2016) which provides crustal velocities from the continuous GNSS measurements at sites collocated to the TG.</p><p>Satellite altimetry data</p><p>Whilst Satellite Altimetry over the open ocean is a mature discipline, global altimetry data collected over the coastal ocean remain still largely unexploited. This is because of intrinsic difficulties in the corrections and issues of land contamination in the footprint that have so far resulted in systematic flagging and rejection of these data. In this work, the relevant methodology to overcome these problems and extend the capabilities of current and future altimeters to the coastal zone (coastal altimetry) will be discussed and a number of coastal altimetry data sets will be used (eg SARvatore, X-TRACK, RADS etc). Finally, a practical example using real data sets over the Aegean Sea will be presented. </p><p> </p><p> </p>

Sea level has been steadily rising over the past century, predominantly due to anthropogenic climate change. The rate of sea level rise will keep increasing with continued global warming, and, even if temperatures are stabilized through the phasing out of greenhouse gas emissions, sea level is still expected to rise for centuries. This will affect coastal areas worldwide, and robust projections are needed to assess mitigation options and guide adaptation measures. Here we combine the equilibrium response of the main sea level rise contributions with their last century's observed contribution to constrain projections of future sea level rise. Our model is calibrated to a set of observations for each contribution, and the observational and climate uncertainties are combined to produce uncertainty ranges for 21st century sea level rise. We project anthropogenic sea level rise of 28-56 cm, 37-77 cm, and 57-131 cm in 2100 for the greenhouse gas concentration scenarios RCP26, RCP45, and RCP85, respectively. Our uncertainty ranges for total sea level rise overlap with the process-based estimates of the Intergovernmental Panel on Climate Change. The "constrained extrapolation" approach generalizes earlier global semiempirical models and may therefore lead to a better understanding of the discrepancies with process-based projections.

In recent years, the earth is burning, whether global warming and its influencing factors, based on time series and gray prediction model, put forward the hypothesis and conclusion, provide the data of various factors, to evaluate the past temperature level, to predict the future global temperature level and its trend and analyze the influencing factors. For problem 1, first, based on the analysis of the global average air temperature data from March 2012-February 2022 and March 2022-October 2022, it is unscientific that the global temperature rise in March 2022 led to a larger rise than in the past 10 years. Second, the ARIMA model and the gray prediction model are used to describe the past global temperature levels and predict the future global temperature levels, respectively. The ARIMA model and the grey prediction model show that the global average annual temperature change has been stable in the past, while the prediction that the average temperature of 20.00°C in 2050 or 2100 is inaccurate. The grey prediction model shows that the global temperature levels will continue to rise in the future, and shows that the average temperature in 2,289 observations is expected to reach 20.00°C. According to the forecast, the global average temperature will not, as long as natural resources are properly used, reach 20.00°C. For problem 2, We first divide the Earth into the southern and northern hemisphere, and second into tropical and southern temperate and northern temperate zones. The linear relationship between global average temperature and the two conditions is discussed separately. According to the results, it significantly shows a strong correlation between the global average temperature and time. If unchecked, the global average temperature will grow over time. Second, using the principal component analysis method shows that may affect the global average temperature in the Middle East energy consumption, global hydropower consumption, world oil consumption, global niobium minerals, the world gas proven reserves, global crude oil production, global cadmium production, and to the results are tested, concluded that the principal component analysis method to influence the main indicators of the global average temperature method is more accurate. Therefore, the suggestions for global temperature change are made: to strive to reduce methane emissions; to further strengthen the strategic deployment and practical actions for energy revolution and low-carbon development, while increasing the intensity of energy low-carbon transformation. For problem 3, We propose the feasibility of this model evaluation and prediction results on slowing down or curbing global warming trends. One is direct emissions reduction, with countries signing contracts to force their carbon emissions and unite against global warming. Reduce heat emissions should be put on everyone's daily living habits, with a limited use of daily necessities, to achieve limited production, limited processing, so as to curb industrial flooding, reduce heat emissions. Second, the indirect emission reduction. With the development of industry, carbon dioxide emissions are constantly increasing, leading to a series of problems such as global warming and sea level rise. Afforestation can absorb the carbon dioxide contained in the atmosphere and reduce the greenhouse gas content in the atmosphere. Therefore, we must realize the importance of afforestation, take appropriate measures to improve the effect of afforestation, and better alleviate global warming.