Global Temperature Trends Research Articles

Effects of atmospheric aerosols on heat stress over South Asia

The recent trend in global mean temperature is expected to increase the frequency, duration, and intensity of heat waves and heat stress all around the world, especially over the Indian subcontinent. To investigate the plausible mechanisms and implications of meteorological feedback of aerosol forcing on heat stress over South Asia, simulations using a regional climate model (RegCM4) coupled with aerosols were carried out from 2015 to 2019. Generally, extreme heat days are observed mainly over the central and western Indo-Gangetic plains (IGPs) during May. The two heat-humidity indices, wet-bulb temperature (WBT) and heat index (HI), showed moderate effects due to aerosol forcing despite the high aerosol loading during the pre-monsoon. The inclusion of aerosols leads to surface cooling (a reduction in maximum surface temperature), with the highest impact over western India (WI) and the IGP. However, this surface cooling due to aerosols is compensated by an increase in near-surface relative humidity (RH), leading to an increase in WBT and HI. This compensating effect of RH on WBT and HI is greater in WI than that in eastern India. Regional heterogeneity in aerosol-induced changes in heat stress has a strong dependence on land-atmosphere interactions. This study clearly shows that aerosol-induced changes in RH play a decisive role in the assessment of heat stress, which answers the link between the prevalence of high heat stress conditions despite high aerosol loading (surface cooling) over the Indian region.

Prediction Of Global Temperature Based on ARIMA And GM Model

Global temperatures have risen rapidly in recent decades .In order to find the rising trend of global temperature, this paper establishes ARIMA model and GM model to fit the past temperature changes and predict the future temperature trend. The results showed that the global average temperature will reach 23.55 °C by 2050. Then through the evaluation index SSE, it is judged that the ARIMA ( 0,1,2 ) model hasthe best prediction effect.

Effective climate sensitivity distributions from a 1D model of global ocean and land temperature trends, 1970–2021

Current theoretically based Earth system models (ESMs) produce Effective Climate Sensitivities (EffCS) that range over a factor of three, with 80% of those models producing stronger global warming trends for 1970–2021 than do observations. To make a more observationally based estimate of EffCS, a 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a full range of three model free parameters are evaluated to produce fits to a range of observed surface temperature trends (± 2σ) from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived EffCS are larger than over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.86 °C global EffCS, ± 34% range 1.48–2.15 °C) than the older Representative Concentration Pathway forcing (RCP6, 2.49 °C global average EffCS, ± 34% range 2.04–2.87 °C). The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone. The results are consistent with previous observation-based studies that concluded EffCS during the observational period is on the low end of the range produced by current ESMs.

Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records

Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks

The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST.

Arctic Amplification in the Community Earth System Models (CESM1 and CESM2)

We compare the Arctic amplification (AA) produced by the two Community Earth System Models CESM1 and CESM2, members of the CEMIP5 (Coupled Models Intercomparison Project phase 5) and CEMIP6 collections, respectively. We find that the CESM1 model reproduces the recent high values of the AA deduced from the observed temperature much better than the CESM2. The correlation coefficient within the 1970–2012 time period between CESM1-simulated AA and the observed one is 0.47, while the CESM2 simulation leads to an anticorrelation of r = −0.53. Even the more successful model (CESM1) is not able to reproduce recent high AA values of 4–5. The main cause of this failure is the model’s overestimate of the rate of increase in the mean global temperature in years post 1990. When the CESM1 model’s simulated trend of the mean global temperature is replaced in the expression for the AA by the observed temperature trend, the correlation coefficient increases from 0.47 to 0.75. The CESM1 model is among the best north American models in AA simulation while the CESM2 model is among the least successful.

Global Surface Temperature Variability and Trends and Data Based Attribution to Carbon Emissions

Global Surface Temperature Variability and Trends and Data Based Attribution to Carbon Emissions

Variability in Projected North American Mean and Extreme Temperature and Precipitation Trends for the 21st Century: Model‐To‐Model Differences Versus Internal Variability

AbstractWhile climate change is expected to alter mean and extreme temperature and precipitation over the 21st century, regional changes in these fields remain difficult to project. For a given future emissions scenario, the uncertainty in future projections can be attributed to either model‐to‐model differences, which can presumably be reduced if models are improved, or internal variability, which cannot be constrained to a single outcome but can be used to quantify a range of plausible outcomes. This study examines the range of possible trends in North American mean and extreme temperature and precipitation across phase 6 of the Coupled Model Intercomparison Project models under the SSP3‐7.0 emissions scenario. While model‐to‐model differences primarily govern the spread in mean and extreme temperature trends across these model simulations, the spread in mean and extreme precipitation trends is dominated by internal variability in many regions. A storylines approach—a type of regression‐based analysis—is used to identify key dynamical drivers that explain the variance in future trends across model simulations. The dynamical drivers considered include trends in global mean surface temperature (GMST) and four common modes of climate variability. Combinations of these drivers can reinforce each other to create high‐impact storylines. For example, model simulations with large positive GMST and El Niño‐Southern Oscillation trends are associated with a large reduction in the annual maximum number of consecutive dry days in the American Southwest. However, limiting these storylines to only models with climate sensitivities consistent with observational constraints reduces the likelihood of a large reduction of consecutive dry days in this region.

Decadal variation of the summer extreme high temperature days in northern Eurasia during 1960–2018

Unprecedented heat waves have been demonstrated sweeping across much of the Northern Hemisphere in recent summers. However, this study reveals that for northern Eurasia (30°–70°N, 10°–130°E), significant increases in summer extreme high temperature days (EHTDs) have already commenced since the mid-1990s, with the peaks centered on the surrounding areas of the Caspian Sea and Lake Baikal. Results indicate that compared with the period of 1960–1994 (P1), during 1995–2018 (P2) high-pressure and anticyclonic anomalies occupy the areas around the Caspian Sea and Lake Baikal, thus suppressing local cloud cover and precipitation, enhancing the solar radiation and high-temperature anomalies. The anticyclonic anomaly over Lake Baikal shows a close relationship with the increasing trend of global temperature, and the anomalous anticyclone over the Caspian Sea is under the influence of the warm North Atlantic and anomalous upper-troposphere jet stream during P2. The warmer than normal North Atlantic leads to the high-pressure anomaly over the Caspian Sea by modulating the Rossby wave activity. In addition, during P2, the temperatures in the middle North Atlantic and Western Europe show greater increments than those over higher latitudes, and thus the temperature gradient enhances the westerly thermal wind, which therefore leads to an anomalous upper-level jet stream to the North of the Caspian Sea. Under this circumstance, the Caspian Sea is located to the right side of the anomalous jet exit, inducing the suppressed upward motion over there and favoring the occurrence of more EHTDs after the mid-1990s.

ІННОВАЦІЙНІСТЬ УПРАВЛІНСЬКИХ ЗАХОДІВ ТА МАРКЕТИНГОВОГО ІНСТРУМЕНТАРІЮ ЗЕЛЕНОЇ ЕКОНОМІКИ В ДІДЖИТАЛІЗОВАНОМУ СУСПІЛЬСТВІ ЗНАНЬ

Risks and challenges associated with the development of Industry 4.0 lead to the transition to the principles of a green economy (circular economy), while the rapid digitalization of society, growing demand for valuable natural resources, especially – for different types of energy, and the threat of global climate changes necessitate the implementation of eco-consciousness and effective green marketing tools in business management. The purpose of the study is to analyze the green economy, marketing, and eco-management measures & tools in terms of their innovativeness and in the context of the rapid development of the modern digitalized society – the knowledge society. The practical significance of the paper lies in the fact that trends in global CO2 concentrations and average global temperatures were predicted with the ISAM model (ISAM Integrated Impacts of Climate Change) for the period up to 2100. According to the modeling results, it was found that without significant human efforts to mitigate the effects of climate change, global temperatures will rise above the critical value by almost 3.5°C, while the introduction of green technologies and the latest marketing eco-strategies, as well as limiting the carbon footprint can improve the situation and help to achieve the goals of climate neutrality (under this scenario, the global temperature will increase by only 0.75°C). In the study it has been determined that innovative marketing eco-tools play an important role in ensuring business profitability and shaping its positive smart image. One of the main reasons for the need to introduce environmental innovations in the field of management and marketing at enterprises is that production activities and inefficient use of natural resources pose significant pressure on the environment. This leads not only to environmental degradation but also to increased resource costs, the need to pay high environmental fines for the damage, etc. If such problems accumulate, businesses become inefficient and unable to generate stable profits, so they cannot be competitive and successful in today's digitalized knowledge society.

Impact of global warming on average annual air temperature in Varaždin

Climate change implies a statistically significant change in the long-term mean state or characteristics of the variability of climate elements. It can be caused by natural and/or anthropogenic factors. Recent global warming is often cited as an example of human-induced climate change. Global warming refers to the increasing trend of the average global surface temperature of the Earth, defined as the global average of the mean annual surface temperature of the ocean, usually to a depth of 30-100 cm, and the mean annual surface temperature of the air above the land at a height of 1-2 m above the ground. In 1988, the World Meteorological Organization (WMO) and the United Nations Environment Program (UNEP) established the International Panel on Climate Change (IPCC), which assesses the state of the climate and the risk of climate change caused by human activities.To project climate change, it is necessary to determine the future emissions of greenhouse gases in the atmosphere. According to the IPCC, with appropriate scenarios of greenhouse gas emissions, an increase in the average global surface temperature of 1.5 °C to 4 °C is predicted by the end of the 21st century. The purpose of this paper is to show the relationship between the trend and variability of the global average of the mean annual surface temperature on Earth and the trend of the mean annual air surface temperature in Varaždin for the period 1949-2021. The results show significant correlations among them because Varaždin is located in an big area of surface air temperature anomalies which has a significant contribution to a global average anomalies in several last decades.

Uncertainty constraints on economic impact assessments of climate change simulated by an impact emulator

Abstract Since many new generation Earth system models (ESMs) have been suggested to overestimate future global warming, the latest report of the Intergovernmental Panel on Climate Change used the constrained range of global warming instead of that in the raw ensemble. However, it is not clear how the constraints of climate change projections potentially reduce the uncertainty of impact assessments. Here, we show that the climate-related uncertainty of the economic impact of climate change in the world can be constrained. By applying an impact emulator, we estimate the economic impacts in nine sectors based on 67 ESMs’ future climate change projections and find that the impacts in eight sectors are closely related to the recent past trend of global mean temperature, which is the metric used for the constraint of global warming projections. Observational constraints lower the upper bound of the aggregate economic impact simulated by the single emulator from 2.9% to 2.5% of the world gross domestic product (the relative reduction of variance is 31%) under the medium greenhouse gas concentration scenarios. Our results demonstrate how advances in climate science can contribute to reducing climate-related uncertainties in impact assessments, while we do not examine uncertainties of emulators and impact models.

21st Century Global and Regional Surface Temperature Projections

AbstractMany countries and regions across the globe broke their surface temperature records in recent years. Recent crippling heat waves swept across the Earth, further sparking concerns about the impending arrival of “tipping points” later in the 21st century. This study analyzes observed global surface temperature trends in three target latitudinal regions: the Arctic Circle, Tropics, and the Antarctic Circle. We show that global warming is accelerating unevenly across the planet, with the Arctic warming at more than three times the average rate of our world. We also analyzed the reliability of latitude‐dependent surface temperature simulations from a suite of Coupled Model Intercomparison Project Phase 6 (CMIP6) models and their multi‐model mean (MMM) by comparing their outputs to observational data sets. We selected the best‐performing models based on their statistical abilities to reproduce historical, latitude‐dependent values adapted from these data sets. The surface temperature projections were calculated from ensemble simulations of the Shared Socioeconomic Pathway 2–4.5 (SSP2–4.5) by the selected CMIP6 models. We estimate the calendar years of when surface temperatures will increase by 1.5, 2.0, and 2.5°C relative to the preindustrial period, both globally and in the three target regions. Our results reaffirm a dramatic, upward trend in projected surface temperatures, with unprecedented acceleration in the Arctic Circle, which could lead to catastrophic consequences across the Earth. Further studies are necessary to determine the most efficient solutions to reduce global warming acceleration and maintain a low SSP, both globally and regionally.

Annual Mean Arctic Amplification 1970–2020: Observed and Simulated by CMIP6 Climate Models

AbstractWhile the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the “best” CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time.

Tectonic degassing drove global temperature trends since 20 Ma.

The Miocene Climatic Optimum (MCO) from ~17 to 14 million years ago (Ma) represents an enigmatic reversal in Cenozoic cooling. A synthesis of marine paleotemperature records shows that the MCO was a local maximum in global sea surface temperature superimposed on a period from at least 19 Ma to 10 Ma, during which global temperatures were on the order of 10°C warmer than at present. Our high-resolution global reconstruction of ocean crustal production, a proxy for tectonic degassing of carbon, suggests that crustal production rates were ~35% higher than modern rates until ~14 Ma, when production began to decline steeply along with global temperatures. The magnitude and timing of the inferred changes in tectonic degassing can account for the majority of long-term ice sheet and global temperature evolution since 20 Ma.

Pliocene evolution of the tropical Atlantic thermocline depth

Abstract. It has been hypothesized that global temperature trends are tightly linked to tropical thermocline depth, and that thermocline shoaling played a crucial role in the intensification of late Pliocene Northern Hemisphere glaciation. The Pliocene thermocline evolution in the Pacific Ocean is well documented and supports this hypothesis, but thermocline records from the tropical Atlantic Ocean are limited. We present new planktonic foraminiferal Mg/Ca, δ18O, and δ13C records from the late Pliocene interval at Ocean Drilling Program Site 959 in the Eastern Equatorial Atlantic (EEA), which we use to reconstruct ocean temperatures and relative changes in salinity and thermocline depth. Data were generated using surface-dwelling Globigerinoides ruber and subsurface-dwelling Neogloboquadrina dutertrei. Reduced gradients between the surface and subsurface records indicate deepening of the EEA thermocline at the end of the mid-Piacenzian Warm Period (mPWP; ∼ 3.3–3.0 Ma). We connect our late Pliocene records to previously published early Pliocene δ18O data from Site 959 and compare these to the Site 1000 in the Caribbean Sea. Over the course of the Pliocene, thermocline changes in the EEA and Caribbean Sea follow similar patterns, with prominent step-wise thermocline deepening between ∼ 5.5 and 4.0 Ma and gradual shoaling up to the mPWP, followed by minor deepening at the end of the mPWP. The tropical thermocline depth evolution of the tropical Atlantic differs from the Pacific, which is characterized by gradual basin-wide shoaling across the Pliocene. These results potentially challenge the hypothesized link between tropical thermocline depth and global climate. The mechanisms behind the periodically divergent Pacific and Atlantic thermocline movements remain speculative. We suggest that they are related to basin geometry and heterogenous temperature evolutions in regions from where thermocline waters are sourced. A positive feedback loop between source region temperature and tropical cyclone activity may have amplified tropical thermocline adjustments.

Future evolution of global land surface air temperature trend based on Coupled Model Intercomparison Project Phase 6 models

AbstractIn recent decades, global warming has been an indisputable fact. The increase in extreme events accompanied by the rise in temperature has huge influences on many aspects of the society and economy globally. As a result, forecasting how global temperatures will change and evolve in the future is crucial, but it is restricted by the accuracy and uncertainty of model simulations. Based on ensemble empirical mode decomposition (EEMD) method, this study first evaluates the performance of the CMIP6 models in simulating different timescale components including high‐frequency components (HFC), low‐frequency components (LFC), and secular trend (ST) of surface air temperature (SAT). The results show that the performance of the CMIP6 models in simulating the ST is better. Following the observation constraint method's correction of CMIP6 model simulations, we investigate the evolution characteristics of future temperature secular trends under different Shared Socioeconomic Pathway (SSP) scenarios. The results show that under SSP245 scenario, the rise of SAT first appeared in northwest and east North America and eastern Europe. The spatial distribution of evolution characteristic under the SSP585 scenario is similar to that of the SSP245 scenario but with a larger magnitude. However, more pronounced differences appear in warming rate, which shows a downward trend over time under the SSP245 scenario and an upward trend with time under the SSP585 scenario.

The Controlling Mechanisms of the Recent Global Warming Hiatus: A Focus on the Internal Variabilities

A flattening trend of global surface temperature from 1998 to 2013 is generally referred to global warming hiatus. In this review, its basics and controlling mechanisms are the focuses with the latter receiving more attention. The mechanisms for the hiatus are largely derived from internal climate variabilities which could be divided into sea surface temperature (SST) and energy variabilities. The major SST variabilities are Interdecadal Pacific Oscillation (IPO) and Atlantic Multi-decadal Oscillation (AMO). The negative phase of IPO during the hiatus is associated with strengthened Pacific trade winds that enhance subsurface heat uptake, which is generally thought to be the predominant mechanism for the hiatus. Furthermore, AMO influences the global surface temperature at decadal time scales, or even the IPO, which are considered to be the main drivers of hiatus. As for the energy variabilities, vertical uptake and interbasin redistribution of heat are also suggested to be capable of leading to a hiatus period. There was significant warming of global ocean below 700 m during the hiatus as an evidence for the storage of excessive heat in deep ocean. And various heat redistribution patterns like those through ITF or AMOC could also play a significant role in regulating the hiatus.

Emergent constraints on future precipitation changes.

Future projections of global mean precipitation change (ΔP) based on Earth-system models have larger uncertainties than projections of global mean temperature changes (ΔT)1. Although many observational constraints on ΔT have been proposed, constraints on ΔP have not been well studied2-5 and are often complicated by the large influence of aerosols on precipitation4. Here we show that the upper bound (95th percentile) of ΔP (2051-2100 minus 1851-1900, percentage of the 1980-2014 mean) is lowered from 6.2 per cent to 5.2-5.7 per cent (minimum-maximum range of sensitivity analyses) under a medium greenhouse gas concentration scenario. Our results come from the Coupled Model Intercomparison Project phase 5 and phase 6 ensembles6-8, in which ΔP for 2051-2100 is well correlated with the global mean temperature trends during recent decades after 1980 when global anthropogenic aerosol emissions were nearly constant. ΔP is also significantly correlated with the recent past trends in precipitation when we exclude the tropical land areas with few rain-gauge observations. On the basis of these significant correlations and observed trends, the variance of ΔP is reduced by 8-30 per cent. The observationally constrained ranges of ΔP should provide further reliable information for impact assessments.

Factors influencing the ground thermal regime in a mid-latitude glacial cirque (Hoyo Empedrado, Cantabrian Mountains, 2006–2020)

Air and near-surface ground temperatures were measured using dataloggers over 14 years (2006–2020) in 10 locations at 2262 to 2471 m.a.s.l. in a glacial cirque of the Cantabrian Mountains. These sites exhibit relevant differences in terms of substrate, solar radiation, orientation, and geomorphology. Basal temperature of snow (BTS) measurements and electrical resistivity tomography of the talus slope were also performed. The mean annual near-surface ground temperatures ranged from 5.1 °C on the sunny slope to 0.2 °C in the rock glacier furrow, while the mean annual air temperature was 2.5 °C. Snow cover was inferred from near-surface ground temperature (GST) data, estimating between 130 and 275 days per year and 0.5 to 7.1 m snow thickness. Temperature and BTS data show that the lowest part of the talus slope and the rock glacier furrow are the coldest places in this cirque, coinciding with a more persistent and thickest snow cover. The highest temperatures coincide with less snow cover, fine-grained soils, and higher solar radiation. Snow cover has a primary role in controlling GST, as the delayed appearance in autumn or delayed disappearance in spring have a cooling effect, but no correlation with mean annual near-surface ground temperatures exists. Heavy rain-over-snow events have an important influence on the GST. In the talus slope, air circulation during the snow-covered period produces a cooling effect in the lower part, especially during the summer. Significant inter-annual GST differences were observed that exhibited BTS limitations. A slight positive temperature trend was detected but without statistically significance and less prominent than nearby reference official meteorological stations, so topoclimatic conditions reduced the more global positive temperature trend. Probable existence of permafrost in the rock glacier furrow and the lowest part of the talus slope is claimed; however, future work is necessary to confirm this aspect.