Global Surface Temperature Anomalies Research Articles

Reconciling warming trends

Any divergence between real-world climate phenomena and prior expectations poses interesting science questions. The case of the apparent slow-down of warming since the record El Nino event in 1997/1998 is no exception. The global mean surface temperature trend was smaller 1 between 1997 and 2013 (0.07±0.08 °C per decade) than over the last 50 years (0.16 ± 0.02 °C per decade), highlighting questions about the mechanisms that regulate decadal variability in the Earth’s temperature. In addition, the warming trend in the most recent 15-year period is near the lower edge of the 5–95% range of projections from a collection of climate models that were part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Why most of the model simulations suggest more warming than has been observed is a second question that deserves further exploration. Short-term fluctuations in global mean surface temperature anomalies have been a perennial focus of public discussions related to climate change. We should expect to see climate changes, by definition, only in the long-term trends that average over stochastic weather and year-to-year fluctuations such as those associated with the El Nino/Southern Oscillation (ENSO), which favoured a cool La Nina phase in the past few years. On decadal timescales, global mean surface temperatures are expected to vary, too. One cause might be the chaotic internal variability of the coupled system of oceans and atmosphere, for example in the tropical Pacific Ocean 2

Decadal variations in the global atmospheric land temperatures

AbstractInterannual to decadal variations in Earth global temperature estimates have often been identified with El Niño Southern Oscillation (ENSO) events. However, we show that variability on time scales of 2–15 years in mean annual global land surface temperature anomalies Tavg are more closely correlated with variability in sea surface temperatures in the North Atlantic. In particular, the cross‐correlation of annually averaged values of Tavg with annual values of the Atlantic Multidecadal Oscillation (AMO) index is much stronger than that of Tavg with ENSO. The pattern of fluctuations in Tavg from 1950 to 2010 reflects true climate variability and is not an artifact of station sampling. A world map of temperature correlations shows that the association with AMO is broadly distributed and unidirectional. The effect of El Niño on temperature is locally stronger, but can be of either sign, leading to less impact on the global average. We identify one strong narrow spectral peak in the AMO at period 9.1 ± 0.4 years and p value of 1.7% (confidence level, 98.3%). Variations in the flow of the Atlantic meridional overturning circulation may be responsible for some of the 2–15 year variability observed in global land temperatures.

Update of the Chronology of Natural Signals in the Near-Surface Mean Global Temperature Record and the Southern Oscillation Index

Time series for the Southern Oscillation Index and mean global near surface temperature anomalies are compared for the 1950 to 2012 period using recently released HadCRU4 data. The method avoids a focused statistical analysis of the data, in part because the study deals with smoothed data, which means there is the danger of spurious correlations, and in part because the El Ni?o Southern Oscillation is a cyclical phenomenon of irregular period. In these situations the results of regression analysis or similar statistical evaluation can be misleading. With the potential controversy arising over a particular statistical analysis removed, the findings indicate that El Nino-Southern Oscillation exercises a major influence on mean global temperature. The results show the potential of natural forcing mechanisms to account for mean global temperature variation, although the extent of the influence is difficult to quantify from among the variability of short-term influences.

Estimation of impulse response of Earth's climate system at short time intervals

A method is described for restoration of the impulse response h(t) of the Earth's climate system (ECS), which is regarded as a time-invariant linear dynamic system whose input is the change in solar constant, and output—the global mean surface temperature anomalies. Search for solution of the ill-posed inverse problem is carried out on a compact set of non-negative, monotonically non-increasing, convex downward functions. This suggests that ECS may be a first-order dynamic system or a set of similar independent subsystems with different time constants. Results of restoration of h(t) at time intervals up to 100 months show that it is a rapidly decreasing function, which does not differ from zero for t>3 months. An estimate of the equivalent time constant gives the average value of 1.04±0.17 months. The sensitivity of the ECS to changes in radiative forcing at the top of the atmosphere is equal to 0.41±0.05KW−1m2.

A Bayesian approach to detecting change points in climatic records

AbstractGiven distinct climatic periods in the various facets of the Earth's climate system, many attempts have been made to determine the exact timing of ‘change points’ or regime boundaries. However, identification of change points is not always a simple task. A time series containing N data points has approximately Nk distinct placements of k change points, rendering brute force enumeration futile as the length of the time series increases. Moreover, how certain are we that any one placement of change points is superior to the rest? This paper introduces a Bayesian Change Point algorithm which provides uncertainty estimates both in the number and location of change points through an efficient probabilistic solution to the multiple change point problem. To illustrate its versatility, the Bayesian Change Point algorithm is used to analyse both the NOAA/NCDC annual global surface temperature anomalies time series and the much longer δ18O record of the Plio‐Pleistocene. Copyright © 2012 Royal Meteorological Society

Measuring global temperatures: Their analysis and interpretation

This book documents how global surface temperature anomalies (GSTAs) and multidecadal trends are obtained. While ocean heat content change is a more robust metric with which to diagnose global warming, GSTAs have become a primary icon in the climate change debate. The book begins with a brief overview chapter of the Earth's radiative energy budget followed by two chapters on measurement approaches to monitoring temperature, including an interesting discussion of temperature scales. Chapters 4–6 concern measuring land and ocean temperatures. Chapters 7 and 8 discuss global networks and how point measurements are converted to obtain global averages. Chapter 9 focuses on changes in time of temperatures, including maximum and minimum values. This is followed by a short chapter on temperature profiles through the atmosphere and a final chapter of recommendations for future observations of this metric.

Prediction of climate nonstationary oscillation processes with empirical mode decomposition

[1] Long-term nonstationary oscillations (NSOs) are commonly observed in climatological data series such as global surface temperature anomalies (GSTA) and low-frequency climate oscillation indices. In this work, we present a stochastic model that captures NSOs within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD. To evaluate the model performance, the model is tested with the synthetic data set from Rossler attractor and with GSTA data. The results of the attractor show that the proposed approach provides a good characterization of the NSOs. For GSTA data, the last 30 observations are truncated and compared to the generated data. Then the model is used to predict the evolution of GSTA data over the next 50 years. The results of the case study confirm the power of the EMD approach and the proposed NSO resampling (NSOR) method as well as their potential for the study of climate variables.

The role of natural climatic variation in perturbing the observed global mean temperature trend

Controversy continues to prevail concerning the reality of anthropogenically-induced climatic warming. One of the principal issues is the cause of the hiatus in the current global warming trend. There appears to be a widely held view that climatic change warming should exhibit an inexorable upwards trend, a view that implies there is no longer any input by climatic variability in the existing climatic system. The relative roles of climatic change and climatic variability are examined here using the same coupled global climatic model. For the former, the model is run using a specified CO2 growth scenario, while the latter consisted of a multi-millennial simulation where any climatic variability was attributable solely to internal processes within the climatic system. It is shown that internal climatic variability can produce global mean surface temperature anomalies of ±0.25 K and sustained positive and negative anomalies sufficient to account for the anomalous warming of the 1940s as well as the present hiatus in the observed global warming. The characteristics of the internally-induced negative temperature anomalies are such that if this internal natural variability is the cause of the observed hiatus, then a resumption of the observed global warming trend is to be expected within the next few years.

Relationships between global precipitation and surface temperature on interannual and longer timescales (1979–2006)

Associations between global and regional precipitation and surface temperature anomalies on interannual and longer timescales are explored for the period of 1979–2006 using the GPCP precipitation product and the NASA‐GISS surface temperature data set. Positive (negative) correlations are generally confirmed between these two variables over tropical oceans (lands). ENSO is the dominant factor in these interannual tropical relations. Away from the tropics, particularly in the Northern Hemisphere mid‐high latitudes, this correlation relationship becomes much more complicated with positive and negative values of correlation tending to appear over both ocean and land, with a strong seasonal variation in the correlation patterns. Relationships between long‐term linear changes in global precipitation and surface temperature are also assessed. Most intense long‐term, linear changes in annual‐mean rainfall during the data record tend to be within the tropics. For surface temperature however, the strongest linear changes are observed in the Northern Hemisphere mid‐high latitudes, with much weaker temperature changes in the tropical region and Southern Hemisphere. Finally, the ratios between the linear changes in zonal‐mean rainfall and temperature anomalies over the period are estimated. Globally, the calculation results in a +2.3%/°C precipitation change, although the magnitude is sensitive to small errors in the precipitation data set and to the length of record used for the calculation. The long‐term temperature‐precipitation relations are also compared to the interannual variations of the same ratio in a zonally averaged sense and are shown to have similar profiles, except for over tropical land areas.

Global surface temperature in relation to northeast monsoon rainfall over Tamil Nadu

The local and teleconnective association between Northeast Monsoon Rainfall (NEMR) over Tamil Nadu and global Surface Temperature Anomalies (STA) is examined using the monthly gridded STA data for the period 1901–2004. Various geographical regions which have significant teleconnective signals associated with NEMR are identified. During excess (deficient) NEMR years, it is observed that the meridional gradient in surface air temperature anomalies between Europe and north Africa, in the month of September is directed from the subtropics (higher latitudes) to higher latitudes (subtropics). It is also observed that North Atlantic Oscillation (NAO) during September influences the surface air temperature distribution over north Africa and Europe. Also, the NAO index in January shows significant inverse relationship with NEMR since recent times. The central and eastern equatorial Pacific oceanic regions have significant and consistent positive correlation with NEMR while the western equatorial region has significant negative correlation with NEMR. A zonal temperature anomaly gradient index (ZTAGI) defined between eastern equatorial Pacific and western equatorial Pacific shows stable significant inverse relationship with NEMR

An analysis of simulated and observed global mean near-surface air temperature anomalies from 1979 to 1999: trends and attribution of causes

The 1979–1999 response of the climate system to variations in solar spectral irradiance is estimated by comparing the global averaged surface temperature anomalies simulated by a 2-D energy balance climate model to observed temperature anomalies. We perform a multiple regression of southern oscillation index and the individual model responses to solar irradiance variations, stratospheric and tropospheric aerosol loading, stratospheric ozone trends, and greenhouse gases onto each of five near-surface temperature anomaly data sets. We estimate the observed difference in global mean near-surface air temperature attributable to the solar irradiance difference between solar maximum and solar minimum to be between 0.06 and 0.11 K, and that 1.1–3.8% of the total variance in monthly mean near-surface air temperature data is attributable to variations in solar spectral irradiance. For the five temperature data sets used in our analysis, the trends in raw monthly mean temperature anomaly data have a large range, spanning a factor of 3 from 0.06 to 0.17 K/decade. However, our analysis suggests that trends in monthly temperature anomalies attributable to the combination of greenhouse gas, stratospheric ozone, and tropospheric sulfate aerosol variations are much more consistent among data sets, ranging from 0.16 to 0.24 K/decade. Our model results suggest that roughly half of the warming from greenhouse gases is cancelled by the cooling from changes in stratospheric ozone. Tropospheric sulfate aerosol loading in the present day atmosphere contributes significantly to the net radiative forcing of the present day climate system. However, because the change in magnitude and latitudinal distribution of tropospheric sulfate aerosol has been small over the past 20 years, the change in the direct radiative forcing attributable to changes in aerosol loading over this time is also small.

Predictability of global surface temperature by means of nonlinear analysis

The time series of annually averaged global surface temperature anomalies for the years 1856–1998 is studied through nonlinear time series analysis with the aim of estimating the predictability time. Detection of chaotic behaviour in the data indicates that there is some internal structure in the data; the data may be considered to be governed by a deterministic process and some predictability is expected. Several tests are performed on the series, with results indicating possible chaotic behaviour.

1973–1996 Trends in depth‐averaged tropospheric temperature

The analysis of thickness derived from the National Center for Environmental Prediction (NCEP) reanalysis indicates that there has been no statistically significant warming trend in layer‐averaged global tropospheric temperatures during the period 1979–1996. While this result is at variance with data based on surface information, NCEP trends and interannual variations are closely related to tropospheric mean‐layer temperatures obtained from the satellite‐based microwave sounding unit (MSU) lower‐tropospheric data set. When a longer period, 1973–1996, is used for the NCEP analysis, a warming trend within a 95% confidence interval is derived. However, it is nearly half the magnitude of the surface‐derived trends. Strong correlations of the yearly global surface temperature anomalies and those calculated for several layers in the NCEP data indicate coincident interannual temperature variations throughout the depth of the troposphere. The disagreement of both our analysis and satellite‐derived estimates with surface data could be due to a land surface measurement bias, a result of land use changes such as deforestation and agricultural expansion whose effects are not yet accounted for in the global temperature records, and/or a decoupling between surface and lower‐tropospheric temperature over the last several years.

A comparison of the CCM1-simulated climates for pre-industrial and present-day CO 2 levels

A comparison is made between the climatic equilibria of the NCAR CCM for a pre-industrial atmospheric CO 2 level (265 ppm) and a current (circa 1975) level of 330 ppm, including statistical estimates of the level of confidence in the implied changes. We also compare the model results to observations compiled over the past 100 years, which corresponds to roughly half of the period of change in CO 2 from pre-industrial (1800) to current levels. A relatively large model response in surface temperature and a smaller response in the precipitation, surface pressure, and storm track fields is obtained. These results are in accord with previous findings of the climate sensitivity to systematic changes in CO 2 forcings. A t-statistic of the model results indicates a significant surface temperature response to a relatively small change in CO 2, above the inherent model variability. Observations of global surface temperature anomalies for the period 1890–1990 show some similarities to the model results, especially a warming in regions of the wintertime northern hemisphere of 2–3° C. A point-by-point correlation of the 330 ppm minus 265 ppm model temperature differences to the observed 1980-1890 differences suggests that some of the variance in the observed trends in the surface temperature anomalies may be explained by the model experiments.

Interdecadal changes of surface temperature since the late nineteenth century

We present global fields of decadal annual surface temperature anomalies, referred to the period 1951–1980, for each decade from 1881–1890 to 1981–1990 and for 1984–1993. In addition, we show decadal calendar‐seasonal anomaly fields for the warm decades 1936–1945 and 1981–1990. The fields are based on sea surface temperature (SST) and land surface air temperature data. The SSTs are corrected for the pre‐World War II use of uninsulated sea temperature buckets and incorporate adjusted satellite‐based SSTs from 1982 onward. Our results extend those published in the 1990 Intergovernmental Panel on Climate Change Scientific Assessment and its 1992 supplement. We assess the impact of various sources of error in the fields. Despite poor data coverage initially and around the two World Wars the generally cold end of the nineteenth century and start to the twentieth century are confirmed, together with the substantial warming between about 1920 and 1940. Slight cooling of the northern hemisphere took place between the 1950s and the mid‐1970s, although slight warming continued south of the equator. Recent warmth has been most marked over the northern continents in winter and spring, but the 1980s were warm almost everywhere apart from Greenland, the northwestern Atlantic and the midlatitude North Pacific. Parts of the middle‐ to high‐latitude southern ocean may also have been cool in the 1980s, but in this area the 1951–1980 climatology is unreliable. The impact of the satellite data is reduced because the record of blended satellite and in situ SST is still too short to yield a climatology from which to calculate representative anomalies reflecting climatic change in the southern ocean. However, we propose a method of using existing satellite data in a step toward this target. The maps are condensed into global and hemispheric decadal surface temperature anomalies. We show the sensitivity of these estimated anomalies to alternative methods of compositing the spatially incomplete fields. Running decadal zonal means and annual global and hemispheric time series are also shown. Finally, we discuss some salient features in terms of observed atmospheric circulation changes and of the results of climate model integrations with increasing atmospheric greenhouse gases.

1992 Brings return to moderate global temperatures

Natural events, such as the Mt. Pinatubo eruption in the Philippines and the El Niño/Southern Oscillation (ENSO) episode in the tropical Pacific Ocean, had major impacts on the global climate in 1992. These phenomena were associated with a return to more moderate global temperatures during 1992 after several years of record or near‐record high temperatures. During the first part of the year, the 1991–1992 ENSO episode contributed to above normal temperatures in the Northern Hemisphere, while cooling during the latter part of the year was associated with the aerosol cloud produced by the June 1991 eruption of Mt. Pinatubo. By the spring of 1992, the stratospheric aerosol cloud had extended from the tropics well into both hemispheres. In this report, global surface temperature anomalies are defined as departures from the 1961–1990 base period means. As the aerosol cloud spread throughout the Northern Hemisphere during a time of increasing solar radiation, the surface temperature anomalies responded by becoming less positive over much of the hemisphere. This relative cooling helped to make 1992 the coolest year since 1986. Temperatures also were dramatically cooler throughout the troposphere.

The Bakerian Lecture, 1991 The predictability of weather and climate

There is large public and political interest in the predictability of weather and climate, in particular in the influence of human activities on the likely climate change during the next century. Numerical models are the main tools which enable the nonlinear processes involved in the dynamics and physics of the atmosphere and other components of the climate system to be integrated in an effective way. The performance of such models used for weather forecasting has continued to improve as more accurate data with better coverage has become available, as improved descriptions of the physics and dynamics have been incorporated and as computing capacity and speed have increased. Studies of the predictability with models suggest that with further improvements in data and models deterministic forecasting of detailed weather may ultimately have useful skill up to 2-3 weeks ahead. Beyond the limit of deterministic forecasting, some skill remains for the forecasting of general weather patterns which can be pursued by studying ensembles of model forecasts from slightly varying initial conditions. The largest difficulty with further improvements of numerical models lies in their inadequate treatment of the motions too small to be explicitly resolved. Interactions between the atmosphere and the ocean are responsible for substantial variations on seasonal, interannual and longer timescales. Forecasts are being provided of seasonal precipitation in the Sahel region of Africa based on a knowledge of global sea surface tem perature (SST) anomalies together with the assumption that such anomalies tend to persist from one season to the next. Attempts to forecast SST anomalies have centred on tropical regions in particular on the El Nino. Simple models show some skill in forecasting El Nino events 3-9 months in advance. Studies with more elaborate models which as yet only show partial success in simulating these events demonstrate the complex nature of the interactions involved. Turning to the likely changes in climate next century: if no changes occur in the atmosphere other than the increase in C0 2 and other greenhouse gases due to human activities, the increase in radiative forcing due to a doubling of atmospheric C0 2 concentration would lead to an increase of about 1.2 °C in global average temperature. Water vapour and ice-albedo feedbacks raise this to a figure of about 2.5 °C (with an uncertainty range of 1.5—4.5 °C) as estimated by the Intergovernmental Panel for Climate Change. Such a change would dominate over forcing likely to arise from other factors, and this estim ated rate of change next century is probably greater than any which has occurred on earth during the past 10000 years. The main uncertainties in climate change predictions arise from the inadequacies of the models in their descriptions of cloud-radiation and ocean circulation feedbacks. Until there is more confidence in the treatment of these feedbacks there are bound to be large uncertainties associated with any predictions of regional climate change. To reduce the uncertainties there need to be improvements in computer power, in model formulation and in our understanding of climate processes together with a large programme of observations of climate parameters to provide early detection of climate change and to provide validation of climate models and to provide data for initialization of model integrations. An important question is whether changes in climate due to changes in radiative forcing are predictable. It is pointed out that the response to climate over the past half million years to changes in forcing due to the variations in the Earth ’s orbit (Milankovitch cycles) is a regular one; some 60% of variations in the global temperature as established from the palaeontological record occur near frequencies of the Milankovitch cycles. We can, therefore, expect the changes in climate due to increasing greenhouse gases to be a largely predictable response. Large, but probably predictable, changes in the circulation of the deep ocean have modified climate change during past epochs and could have significant influence on future climate change.