Global Surface Temperature Anomalies Research Articles

A Simple Relationship Between the Magnitude and Spatial Extent of Global Surface Temperature Anomalies

AbstractPreparing for climate change requires an understanding of the degree to which global warming has regional implications. Here we document a strong relationship between the magnitude and extent of warming and explain its origin using a simple model based on binomial statistics. Applied to HadCRUT5 instrumental observations, the model shows that 96% of interannual variability in the proportion of regions experiencing anomalous warmth over the last century can be explained on the basis of the magnitude of global mean surface temperature (GMST) anomalies. The model performs similarly well when applied to a variety of unforced and forced model simulations and represents a general thermodynamic link between global and local warming on annual timescales. Our model predicts that, independent of the baseline that is chosen, 95% of the globe is expected to experience above‐average annual temperatures at 0.7°C of GMST warming, and 99% at 1.0°C of warming.

Autocorrelation and Parameter Estimation in a Bayesian Change Point Model

A piecewise function can sometimes provide the best fit to a time series. The breaks in this function are called change points, which represent the point at which the statistical properties of the model change. Often, the exact placement of the change points is unknown, so an efficient algorithm is required to combat the combinatorial explosion in the number of potential solutions to the multiple change point problem. Bayesian solutions to the multiple change point problem can provide uncertainty estimates on both the number and location of change points in a dataset, but there has not yet been a systematic study to determine how the choice of hyperparameters or the presence of autocorrelation affects the inference made by the model. Here, we propose Bayesian model averaging as a way to address the uncertainty in the choice of hyperparameters and show how this approach highlights the most probable solution to the problem. Autocorrelation is addressed through a pre-whitening technique, which is shown to eliminate spurious change points that emerge due to a red noise process. However, pre-whitening a dataset tends to make true change points harder to detect. After an extensive simulation study, the model is applied to two climate applications: the Pacific Decadal Oscillation and a global surface temperature anomalies dataset.

Explaining Forcing Efficacy With Pattern Effect and State Dependence

AbstractThe magnitude of global surface temperature change in response to unit radiative forcing depends on the type and magnitude of forcing agent—a concept known as a “forcing efficacy.” However, the mechanisms behind the forcing efficacy are still unclear. In this study, we perform a set of simulations using CESM1 to calculate the efficacy of 10 different forcing agents defined in terms of fixed‐SST effective radiative forcing, and then use a Green's function approach to show that each forcing efficacy can be largely understood in terms of the radiative feedbacks associated with the different surface temperature patterns induced by the forcing agents (a pattern effect). We also quantify how the state dependence of feedbacks on global mean surface temperature anomalies impacts forcing efficacies. The results show that the forcing efficacy can be well reconstructed with a combination of pattern effect and state dependence.

An Assessment of the Impact of Temperature Rise Due to Climate Change on Asphalt Pavement in China

In the global warming context, understanding the impact of temperature rise on asphalt pavement is the basis for making adaptation strategies. An approach based on historical climate data and pavement performance models was employed to assess the potential impact of temperature rise on asphalt pavement in China. It is shown that permanent deformation is one sensitive aspect of asphalt pavement performance, which increased on average by 20.70% from 1992 to 2019. Another one is low-temperature cracking, which decreased by 20.99% from 1970 to 1997, but has remained almost unchanged since 1997. Global mean surface temperature anomalies of 1.5 °C and 2.0 °C will increase the permanent deformation of asphalt pavement by 18.63% and 36.71%, respectively, compared to 2019. Global warming is bringing serious challenges to the structure and material design of asphalt pavement due to the increasing service temperature range.

An Intraseasonal Mode Linking Wintertime Surface Air Temperature over Arctic and Eurasian Continent

AbstractKey processes associated with the leading intraseasonal variability mode of wintertime surface air temperature (SAT) over Eurasia and the Arctic region are investigated in this study. Characterized by a dipole distribution in SAT anomalies centered over north Eurasia and the Arctic, respectively, and coherent temperature anomalies vertically extending from the surface to 300 hPa, this leading intraseasonal SAT mode and associated circulation have pronounced influences on global surface temperature anomalies including the East Asian winter monsoon region. By taking advantage of realistic simulations of the intraseasonal SAT mode in a global climate model, it is illustrated that temperature anomalies in the troposphere associated with the leading SAT mode are mainly due to dynamic processes, especially via the horizontal advection of winter mean temperature by intraseasonal circulation. While the cloud–radiative feedback is not critical in sustaining the temperature variability in the troposphere, it is found to play a crucial role in coupling temperature anomalies at the surface and in the free atmosphere through anomalous surface downward longwave radiation. The variability in clouds associated with the intraseasonal SAT mode is closely linked to moisture anomalies generated by similar advective processes as for temperature anomalies. Model experiments suggest that this leading intraseasonal SAT mode can be sustained by internal atmospheric processes in the troposphere over the mid- to high latitudes by excluding forcings from Arctic sea ice variability, tropical convective variability, and the stratospheric processes.

The ERA5 global reanalysis: Preliminary extension to 1950

AbstractThe extension of the ERA5 reanalysis back to 1950 supplements the previously published segment covering 1979 to the present. It features the assimilation of additional conventional observations, as well as improved use of early satellite data. The number of observations assimilated increases from 53,000 per day in early 1950 to 570,000 per day by the end of 1978. Accordingly, the quality of the reanalysis improves throughout the period, generally joining seamlessly with the segment covering 1979 to the present. The fidelity of the extension is illustrated by the accurate depiction of the North Sea storm of 1953, and the events leading to the first discovery of sudden stratospheric warmings in 1952. Time series of ERA5 global surface temperature anomalies show temperatures to be relatively stable from 1950 until the late 1970s, in agreement with the other contemporary full‐input reanalysis covering this period and with independent data sets, although there are significant differences in the accuracy of representing specific regions, Europe being well represented in the early period but Australia less so. The variability of ERA5 precipitation from month to month agrees well with observations for all continents, with correlations above 90% for most of Europe and generally in excess of 70% for North America, Asia and Australia. The evolution of upper air temperatures, humidities and winds shows smoothly varying behaviour, including tropospheric warming and stratospheric cooling, modulated by volcanic eruptions. The Quasi‐Biennial Oscillation is well represented throughout. Aspects to be improved upon in future reanalyses include the assimilation of tropical cyclone data, the spin‐up of soil moisture and stratospheric humidity, and the representation of surface temperatures over Australia.

Drivers and projections of global surface temperature anomalies at the local scale

More than half of the world’s population now lives in urban areas, and trends in rural-to-urban migration are expected to continue through the end of the century. Although cities create efficiencies that drive innovation and economic growth, they also alter the local surface energy balance, resulting in urban temperatures that can differ dramatically from surrounding areas. Here we introduce a global 1 km resolution data set of seasonal and diurnal anomalies in urban surface temperatures relative to their rural surroundings. We then use satellite-observable parameters in a simple model informed by the surface energy balance to understand the dominant drivers of present urban heating, the heat-related impacts of projected future urbanization, and the potential for policies to mitigate those damages. At present, urban populations live in areas with daytime surface summer temperatures that are 3.21 ∘C (−3.97, 9.24, 5th–95th percentiles) warmer than surrounding rural areas. If the structure of cities remains largely unchanged, city growth is projected to result in additional daytime summer surface temperature heat anomalies of 0.19 ∘C (−0.01, 0.47) in 2100—in addition to warming due to climate change. This is projected to raise the urban population living under extreme surface temperatures by approximately 20% compared to current distributions. However we also find a significant potential for mitigation: 82% of all urban areas have below average vegetation and/or surface albedo. Optimizing these would reduce urban daytime summer surface temperatures for the affected populations by an average of −0.81 ∘C (−2.55, −0.05).

The impact of temperature anomalies on commodity futures

ABSTRACT Recent evidence points to global warming and climate change as the biggest issues of the century; thus, the analysis of the weather-commodity futures prices relationship has crucial importance. This paper considers the relationship between weather anomalies, proxied by the Global Historical Surface Temperature Anomalies (HadCRUT4), and futures prices of agricultural products, energy commodities, industrial, and precious metals. Analyzing the monthly data between December 1982 and November 2020, the outcomes of the novel Granger causality test suggest unidirectional causality from the temperature anomalies to commodity futures prices. The findings imply that global temperature anomalies impact the expectations about the agricultural- and energy-related economic activities, including the use of commercial and organic fertilizers and fossil fuel combustion, respectively.

Decadal Attribution of Historic Temperature and Ocean Heat Content Change to Anthropogenic Emissions

AbstractWe present an alternative method of calculating the historical effective radiative forcing using the observed temperature record and a kernel based on the 5th Climate Model Intercomparison Project (CMIP5) temperature response. This estimate is the effective radiative forcing time series that the average climate model would need to simulate the observed global mean surface temperature anomalies. We further infer the anthropogenic aerosols radiative forcing as a residual using the better‐known greenhouse gas radiative forcing. This allows an independent estimate of anthropogenic aerosol radiative forcing, which suggests a cooling influence due to aerosols in the early part of the twentieth century. The temporal kernels are also used to calculate decadal contributions from the dominant forcing agents to present‐day temperature, ocean heat content, and thermosteric sea level rise. The current global mean temperature anomaly is dominated by emissions in the past two decades, while current ocean heat content is more strongly affected by earlier decades.

A continuous global record of near-surface soil freeze/thaw status from AMSR-E and AMSR2 data

ABSTRACT Long term and consistent records of near-surface soil freeze/thaw (F/T) status are required for understanding hydrological, ecological, and biogeochemical responses of land surface to global warming. To create such a record, we compiled and inter-calibrated satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its successor, AMSR2, using linear regression models, and then applied a discriminant algorithm to the calibrated observations to map global F/T status from 2002 to 2018. The new global F/T dataset was rigorously assessed using in situ air and surface temperatures, and modelled soil temperature. Results show that agreement between remotely sensed F/T status and that determined by in situ or modelled temperature exceeds 85% and 79% for ascending and descending orbits, respectively. Moreover, consistency between the F/T datasets derived from two sensors is around 0.8 after calibration, in nonoverlapping time frames. With such an accuracy and consistency, we calculated frost days and frost trends using the F/T dataset. The mean annual number of frost days of high northern latitudes (>45° N) is 279.2 ± 44.1 days. Based on Mann-Kendall’s tau-b test, 7.7% of global lands show a significant warming trend, and most of which are concentrated in the Western United States, Northern and Eastern Canada, Northern Europe and Western China. Such a spatial distribution was found to be consistent with the global land surface temperature anomalies trend from 2002 to 2018. Both the results of applications and favourable accuracy indicate the potential of this long, consistent F/T record to track global temperature change.

The “Ocean Stabilization Machine” May Represent a Primary Factor Underlying the Effect of “Global Warming on Climate Change”

Contemporary references to global warming pertain to the dramatic increase in monthly global land surface temperature (GLST) anomalies since 1976. In this paper,we argue that recent global warming is primarily a result of natural causes; we have established three steps that support this viewpoint. The first is to identify periodic functions that perfectly match all of the monthly anomaly data for GLST; the second is to identify monthly sea surface temperature (SST) anomalies that are located within different ocean basin domains and highly correlated with the monthly GLST anomalies; and the third is to determine whether the dramatically increasing (or dramatically decreasing) K-line diagram signals that coincide with GLST anomalies occurred in El Ni&#241o years (or La Ni&#241a years). We have identified 15,295 periodic functions that perfectly fit the monthly GLST anomalies from 1880 to 2013 and show that the monthly SST anomalies in six domains in different oceans are highly correlated with the monthly GLST anomalies. In addition,most of the annual dramatically increasing GLST anomalies occur in El Ni&#241o years; and most of the annual dramatically decreasing GLST anomalies occur in La Ni&#241a years. These findings indicate that the “ocean stabilization machine” might represent a primary factor underlying the effect of “global warming on climate change”.

Can an ensemble climate simulation be used to separate climate change signals from internal unforced variability?

The contribution of internal unforced variability to climate change is explored using a 100-member ensemble climate simulation for the period 1850–2005. The ensemble simulation is based on the Max Planck Institute for Meteorology climate model, ECHAM6, where all members have been exposed to the identically same radiative forcing. The range of global mean surface temperature warming over the 1850–2005 period, based on all members, is 0.65–1.10 °C. The distribution of the global mean surface temperature about the ensemble mean has a standard deviation (StD) of ~ 0.14 °C which slowly decreases in time. Regionally, the largest decrease in the ensemble StD occurs in the Northern Hemisphere winter. Comparing the temporal StD with that from the observed HadCRUT4 surface temperature data indicates that the majority of the ensemble members have a larger temporal StD than the observations suggesting that the model simulations might overestimate the variance. There are clear random 20-year linear trends in global mean surface temperature anomalies as well as significant regional 50-year linear trends. Even with an ensemble mean warming trend, typical of the early twenty-first century, a global hiatus in temperature of 20 years duration is possible to occur by chance. The results support the view that observed decadal and multi-decadal anomalies in the twentieth century were significantly influenced by internal processes of the climate system. This is particularly the case for the observed global warming trend of 1910–1940 and the global cooling trend of 1940–1970. Global mean precipitation hardly increases with time in the ensemble simulations, but in agreement with theory regional changes occur, with increasing precipitation in polar regions and in some tropical areas. In the subtropics there are reductions in precipitation. Long-lasting regional anomalies of significant amplitudes occur by chance in the ensemble integration.

The relation between natural variations in ocean heat uptake and global mean surface temperature anomalies in CMIP5

It is still unclear whether a hiatus period arises due to a vertical redistribution of ocean heat content (OHC) without changing ocean heat uptake (OHU), or whether the increasing radiative forcing is associated with an increase in OHU when global mean surface temperature (GMST) rise stalls. By isolating natural variability from forced trends and performing a more precise lead-lag analysis, we show that in climate models TOA radiation and OHU do anti-correlate with natural variations in GMST, when GMST leads or when they coincide, but the correlation changes sign when OHU leads. Surface latent and sensible heat fluxes always force GMST-variations, whilst net surface longwave and solar radiation fluxes have a damping effect, implying that natural GMST-variations are caused by oceanic heat redistribution. In the models an important trigger for a hiatus period on decadal timescales is increased reflection of solar radiation, by increased sea-ice cover over deep-water formation areas. On inter-annual timescales, reflection of solar radiation in the tropics by increased cloud cover associated with La Niña is most important and the subsequent reduction in latent heat release becomes the dominant cause for a hiatus.

The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity

Abstract. Our climate is constrained by the balance between solar energy absorbed by the Earth and terrestrial energy radiated to space. This energy balance has been widely used to infer equilibrium climate sensitivity (ECS) from observations of 20th-century warming. Such estimates yield lower values than other methods, and these have been influential in pushing down the consensus ECS range in recent assessments. Here we test the method using a 100-member ensemble of the Max Planck Institute Earth System Model (MPI-ESM1.1) simulations of the period 1850–2005 with known forcing. We calculate ECS in each ensemble member using energy balance, yielding values ranging from 2.1 to 3.9 K. The spread in the ensemble is related to the central assumption in the energy budget framework: that global average surface temperature anomalies are indicative of anomalies in outgoing energy (either of terrestrial origin or reflected solar energy). We find that this assumption is not well supported over the historical temperature record in the model ensemble or more recent satellite observations. We find that framing energy balance in terms of 500 hPa tropical temperature better describes the planet's energy balance.

Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

AbstractThis study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1244 rainfall stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: trend can be attributed to changes in global surface temperature anomalies or to a combination of well-known cyclical climate modes with varying quasiperiodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypothesis is made based on the Watanabe–Akaike information criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the U.S. Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific decadal oscillation, and the Atlantic multidecadal oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the U.S. Northwest, West, and Southwest climate regions.

Precipitation, temperature, and moisture transport variations associated with two distinct ENSO flavors during 1979–2014

Interannual precipitation and temperature variations during 1979–2014 are investigated by examining the effects of two distinct flavors of the El Nino-Southern Oscillation (ENSO), i.e., the tropical eastern Pacific (EP) and central Pacific (CP) ENSO events. Satellite- and ground-based observations with global coverage are applied including the monthly precipitation data from the Global Precipitation Climatology Project (GPCP) and surface temperature anomalies from the NASA-GISS surface temperature anomaly analysis. Related variations in other water-cycle components including atmospheric moisture transport are also examined by using the outputs from the NASA-Modern Era Retrospective-analysis for Research and Applications (MERRA). While the second leading mode from an EOF analysis of sea surface temperature (SST) anomalies between 30°N and 30°S is dominated by interdecadal-scale variability that is not a focus of this study, the first and third leading modes represent well the EP and CP events, respectively. The corresponding principal components (PC1 and PC3) are then applied as indices to estimate the influences of the two ENSO flavors on various physical components through linear regression. Because of their distinct SST configurations in the tropical Pacific, the two ENSO flavors manifest different spatial features of precipitation anomalies as shown in past studies. Differences can also be readily seen in satellite-retrieved tropospheric layered temperatures and oceanic columnar water vapor content. General agreements between observations and MERRA outputs can be obtained as judged by consistent respective anomalies corresponding to the two ENSO flavors, suggesting that MERRA could provide an accurate account of variations on the interannual time scale. Interannual variations in MERRA vertically integrated moisture transport (VIMT) are further examined to explore possible relations between precipitation and tropospheric moisture transport corresponding to the two flavors during two contrasting seasons: December–March (DJFM) and June–September (JJAS). Anomalies of zonal moisture transport in the deep tropics following the variations in the Pacific Walker Circulation are distinctly different for two ENSO flavors and also manifest evident seasonal variations for each flavor. Differences in the zonal mean VIMT (both zonal and meridional components) are also evident between the two flavors, consistent with the differences in zonal mean precipitation anomalies from both GPCP and MERRA. Furthermore, the ENSO flavors are associated with distinct precipitation anomaly patterns over various land areas, which can be further traced to the differences in their associated VIMT anomalies, particularly during DJFM when the warm ENSO events usually reach their mature phase.

The Atlanto-Pacific multidecade oscillation and its imprint on the global temperature record

Results from a multiregression analysis of the global and sea surface temperature anomalies for the period 1950–2011 are presented where among the independent variables multidecade oscillation signals over various oceanic areas are included. These indices are defined in analogy with the Atlantic Multidecadal Oscillation (AMO) index. Unexpectedly we find that a strong multidecade oscillation signal echoing the AMO is also present in the Western and Northwestern Pacific region. The results indicate that naturally induced climate variations seem to be dominated by two internal variability modes of the ocean–atmosphere system: AMO and El Niño Southern Oscillation, with a marked geographical dichotomy in their respective areas of dominance. As the AMO index is directly derived from SST data the finding that the AMO signal is present on a large fraction of the global oceanic surface casts doubt on its use as an independent explanatory variable in regression analyses of the global surface temperature anomalies.

On the causal structure between CO2 and global temperature

We use a newly developed technique that is based on the information flow concept to investigate the causal structure between the global radiative forcing and the annual global mean surface temperature anomalies (GMTA) since 1850. Our study unambiguously shows one-way causality between the total Greenhouse Gases and GMTA. Specifically, it is confirmed that the former, especially CO2, are the main causal drivers of the recent warming. A significant but smaller information flow comes from aerosol direct and indirect forcing, and on short time periods, volcanic forcings. In contrast the causality contribution from natural forcings (solar irradiance and volcanic forcing) to the long term trend is not significant. The spatial explicit analysis reveals that the anthropogenic forcing fingerprint is significantly regionally varying in both hemispheres. On paleoclimate time scales, however, the cause-effect direction is reversed: temperature changes cause subsequent CO2/CH4 changes.

An exact approach to Bayesian sequential change point detection

Change point models seek to fit a piecewise regression model with unknown breakpoints to a data set whose parameters are suspected to change through time. However, the exponential number of possible solutions to a multiple change point problem requires an efficient algorithm if long time series are to be analyzed. A sequential Bayesian change point algorithm is introduced that provides uncertainty bounds on both the number and location of change points. The algorithm is able to quickly update itself in linear time as each new data point is recorded and uses the exact posterior distribution to infer whether or not a change point has been observed. Simulation studies illustrate how the algorithm performs under various parameter settings, including detection speeds and error rates, and allow for comparison with several existing multiple change point algorithms. The algorithm is then used to analyze two real data sets, including global surface temperature anomalies over the last 130 years.

Scale-dependency of the global mean surface temperature trend and its implication for the recent hiatus of global warming.

Studies of the global mean surface temperature trend are typically conducted at a single (usually annual or decadal) time scale. The used scale does not necessarily correspond to the intrinsic scales of the natural temperature variability. This scale mismatch complicates the separation of externally forced temperature trends from natural temperature fluctuations. The hiatus of global warming since 1999 has been claimed to show that human activities play only a minor role in global warming. Most likely this claim is wrong due to the inadequate consideration of the scale-dependency in the global surface temperature (GST) evolution. Here we show that the variability and trend of the global mean surface temperature anomalies (GSTA) from January 1850 to December 2013, which incorporate both land and sea surface data, is scale-dependent and that the recent hiatus of global warming is mainly related to natural long-term oscillations. These results provide a possible explanation of the recent hiatus of global warming and suggest that the hiatus is only temporary.