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Ocean variability and its influence on the detectability of greenhouse warming signals
Abstract
Recent investigations have considered whether it is possible to achieve early detection of greenhouse-gas-induced climate change by observing changes in ocean variables. In this study, we use model data to assess some of the uncertainties involved in estimating when we could expect to detect ocean greenhouse warming signals. We distinguish between detection periods and detection times. As defined here, detection period is the length of a climate time series which must be available in order to detect a given linear trend in the presence of the natural climate variability. Detection period is defined in model years and is independent of reference time and the real time evolution of the signal. Detection time is computed for an actual time-evolving signal from a greenhouse warning experiment and depends on the experiment`s start date. Two sources of uncertainty are considered - those associated with the level of natural variability or noise, and those associated with the time-evolving signals. We analyze the ocean signal and noise for spatially-averaged ocean circulation indices such as ice volume, heat and fresh water fluxes, rate of deep water formation, salinity, temperature, and transport of mass. The signals for these quantities are taken from recent time-dependent greenhouse warming experiments performed by the Hamburg group with a coupled ocean-atmosphere General Circulation Model. The natural variability noise is derived from a 300-year control run performed with the same coupled atmosphere-ocean model and from two long (> 3,000 year) stochastic forcing experiments in which an uncoupled ocean model was forced by white-noise surface flux variations. In the first experiment the stochastic forcing was restricted to the fresh water fluxes, while in the second experiment the ocean model was additionally forced by variations in wind stress and heat fluxes. The mean states and ocean variability are very different in the three natural variability integrations.
... A high similarity between the observed (Fig. 1D) and simulated (Fig. 1E) distinguished from noise, by what year did this occur? One of the objective methods to answer the question is so-called optimal fingerprint analysis (37)(38)(39). We conducted the optimal fingerprint analysis using observations and the model simulations (Optimal Fingerprint Analysis). ...
... Therefore, it would be preferable to adopt a multiple-model approach to reduce the uncertainty. Another caveat is that in the optimal fingerprint analysis the detection time was computed based on the statistics using linear trends following previous studies (37)(38)(39). However, as indicated in Fig. 1A, the observed warming trend is much stronger since 1980 than before. ...
... Optimal Fingerprint Analysis. We applied an optimal fingerprint analysis method (37)(38)(39) to determine if expected spatial patterns of trends in TCF induced by external forcing derived from the models could be identified in the observations. A detailed description is also available in Santer et al. (39). ...
Owing to the limited length of observed tropical cyclone data and the effects of multidecadal internal variability, it has been a challenge to detect trends in tropical cyclone activity on a global scale. However, there is a distinct spatial pattern of the trends in tropical cyclone frequency of occurrence on a global scale since 1980, with substantial decreases in the southern Indian Ocean and western North Pacific and increases in the North Atlantic and central Pacific. Here, using a suite of high-resolution dynamical model experiments, we show that the observed spatial pattern of trends is very unlikely to be explained entirely by underlying multidecadal internal variability; rather, external forcing such as greenhouse gases, aerosols, and volcanic eruptions likely played an important role. This study demonstrates that a climatic change in terms of the global spatial distribution of tropical cyclones has already emerged in observations and may in part be attributable to the increase in greenhouse gas emissions.
... Detection of long-term linear trends can be affected by numerous factors, such as the length of data available, the magnitude of the trend to be detected, the degree of variability (e.g. variance and autocorrelation, or short-term memory), discontinuity or gaps within the timeseries, observation error, and the trend detection technique used (Santer et al., 1995;Weatherhead et al., 1998;Vellinga and Wood, 2004;Baehr et al., 2007Baehr et al., , 2008Keller et al., 2007a,b;Roberts and Palmer, 2012;Roberts et al., 2014;Williams et al., 2015). Using an approach that accounts for autocorrelation by generating lag-1 ARMA Our aim is to estimate the number of years required to detect a long-term AMOC decline using 20 future scenario RCP8.5 CMIP5 models and RAPID observations. ...
... We also show that the strength of the trend has the highest impact on detectability, with the lowest n* among the CMIP5 and RAPID simulations being the result of the strongest trends (Table 3.1), despite the comparatively high variance and AR(1) coefficient in the RAPID data, for example. Although other studies have accounted for the AMOC's autocorrelation by using different techniques, such as generation of ARMA simulations , calculation of the standard error of the trend after determining of the number of truly independent degrees of freedom , and bootstrapping techniques (Baehr et al., 2007), here AR simulations and GLS is used (Santer et al., 1995;Vellinga and Wood, 2004;Baehr et al., 2008;Keller et al., 2007a,b;Roberts and Palmer, 2012). We show that the use of OLS instead of GLS can slightly underestimate RAPID's n* by 3 years (25 instead of 28 years in Table 3.1) or overestimate the proportion of true positives after 13 years (to 50% instead of 40% in Figure 3.3). ...
... 2015a)), 80 years due to sampling gaps of 10-year intervals (Baehr et al., 2008), or 100 years due to high natural variability (Santer et al., 1995). Our results support that since a significant decline is detectable after 28 to 35 years, these studies can rightly detect the timing of emergence using model output, however RAPID still requires more observations before performing such an analysis. ...
The Atlantic Meridional Overturning Circulation (AMOC) is a key mechanism of the global coupled ocean-atmosphere climate system, primarily via the redistribution of heat. The northward transport of warm, salty near-surface waters from the southern hemisphere is a unique feature of the Atlantic Ocean, where paleoclimate records have associated past shutdowns of the AMOC with abrupt cooling periods, potentially lasting over a millennium. According to future projections produced by the Coupled Model Intercomparison Project Phase 5 (CMIP5), an AMOC shutdown is unlikely in the next century, although a weakening is very likely, under climate change scenarios. On the other hand, studies estimating the past and current AMOC transport struggle to reach a consensus regarding whether the AMOC has recently undergone or is undergoing a transient decline, and if so, whether it is due to current anthropogenic climate change. This is due to the complexity of the relative contribution of natural and anthropogenic forcings on AMOC variability, and limited direct observational records; the longest continuous trans-basin array being RAPID, which started in 2004. Determining how many years are required to detect a statistically significant AMOC decline is therefore the starting point of this study. From simulations of an artificial AMOC timeseries, generated based on statistical properties of the RAPID and CMIP5 AMOC, an AMOC decline is detectable after 28 to 35 years (i.e., over a decade more than current observations). This reinforces the demand for improved proxy estimates, and defines the incentive behind the other analyses in this study; to investigate potential reconstructions or indicators of the AMOC’s variability and trend in the past and the future. Using a multi-model approach to test the robustness, a proxy, that is presumably dynamically associated to the AMOC, is explored; sea surface height (SSH). The relationship between the AMOC and SSH is shown to be inconsistent across the CMIP5 models and therefore fails tore construct the AMOC’s inter annual variability or multi-decadal trend, using a 13-yeartraining period. This suggests that tidal gauge data can not be used to extend the current RAPID data in the past to identify if a long-term decline has been occurring. To further characterise the potential of an AMOC slowdown, a past and future trend probability analysis is explored using the CMIP5 database. Using 250 years of historical and future scenario data reveals that forced ensemble mean AMOC trends shift the probability of a decline outside its range of natural variability (which is estimated from the control runs), after a sustained 5-year decline or longer. This suggests that inter annual AMOC events are not significantly affected by anthropogenic forcing compared to their natural variability. Furthermore, under the ‘business-as-usual’ scenario (RCP8.5)the probability of a 20-year decline remains high (at 86.5%), and the probability of an ‘intense’ decline reaches a maximum of 55.7% (vs. 13.2% in the historical scenario);in a ‘stabilisation’ scenario (RCP4.5) the trend probability recovers its pre-industrial values by 2100. A 20-year rogue period is identified from 1995 to 2015, marked by simultaneous unique features in the AMOC and salinity transport that are not replicated over any other 20-year period within the 250-year timeseries. These features include the maximum probability and magnitude of an ‘intense’ AMOC decline, and a sustained20-year decline in subpolar salinity transport caused by internal oceanic (as opposed to atmospheric) feedbacks. This work therefore highlights the potential use of direct observations (after another decade of data), and ensemble mean numerical models tore present changes in past, present, and future natural and forced AMOC variability. Such an understanding can be used to improve future climate risk mitigation strategies and planning, with global socio-economic importance in the 21st century.
... We use a pattern-based detection and attribution method that was initially developed at Lawrence Livermore National Laboratory ( [18][19][20][21] ). In this model, the expected response to external forcing is calculated by averaging forced model simulations to decrease internal variability, which is expected to be uncorrelated across separate model runs. ...
... As typical in the detection and attribution literature, we define the signal S(L) to be the linear trend in the L-length projection time series 18 . This signal is assessed for significance against a 'noise' term that quantifies natural climate variability 19 . In most cases, the signal is estimated from observational datasets 21,29 and climate noise is estimated by projecting the output of general circulation models (GCMs) that were run under unforced preindustrial 20 or pastmillennium 30 conditions onto the fingerprint. ...
... There are three components in a detection and attribution study. The fingerprint F(θ, φ) of climate change is the spatial pattern, generally a function of latitude θ and longitude φ, that characterizes the climate system response to external forcing [18][19][20][21] . In much of the literature (for example, refs. ...
Although anthropogenic climate change is expected to have caused large shifts in temperature and rainfall, the detection of human influence on global drought has been complicated by large internal variability and the brevity of observational records. Here we address these challenges using reconstructions of the Palmer drought severity index obtained with data from tree rings that span the past millennium. We show that three distinct periods are identifiable in climate models, observations and reconstructions during the twentieth century. In recent decades (1981 to present), the signal of greenhouse gas forcing is present but not yet detectable at high confidence. Observations and reconstructions differ significantly from an expected pattern of greenhouse gas forcing around mid-century (1950–1975), coinciding with a global increase in aerosol forcing. In the first half of the century (1900–1949), however, a signal of greenhouse-gas-forced change is robustly detectable. Multiple observational datasets and reconstructions using data from tree rings confirm that human activities were probably affecting the worldwide risk of droughts as early as the beginning of the twentieth century. Multiple observational datasets and reconstructions using data from tree rings confirm that human activities were probably affecting the worldwide risk of droughts as early as at the beginning of the twentieth century.
... Here, a fingerprint can be defined as a low or single dimension series that plays a principal role in highdimensional climate time series. According to Santer et al. (1995), a fingerprint applied to 'historical' scenarios is the direction in which a signal induced by a historical climate condition is expected to lie. Just as in the signal strength (Eq. ...
... Using the above methodology, the trend of noise time can have multiple outcomes. Lastly, the magnitude of noise (m) can be estimated using the multiple results obtained by Eq. (5) (Santer et al., 1995). ...
... The signal to noise ratio (SNR) is examined by using the winter streamflow obtained from the median values (most likely values) of the posterior parameter set. It may be more reasonable that its own 'piControl' scenario of the GCM is employed instead of the pooled 'piControl' scenario to reflect the characteristics of each model (Santer et al., 1995). SNR results for the Connecticut River Basin are shown in Fig. 10. ...
In spite of recent popularity for investigating human-induced climate change in regional areas, understanding the contributors to the relative uncertainties in the process remains unclear. To remedy this, this study presents a statistical framework to quantify relative uncertainties in a detection and attribution study. Primary uncertainty contributors are categorized into three types: climate data, hydrologic, and detection uncertainties. While an ensemble of climate models is used to define climate data uncertainty, hydrologic uncertainty is defined using a Bayesian approach. Before relative uncertainties in the detection and attribution study are quantified, an optimal fingerprint-based detection and attribution analysis is employed to investigate changes in winter streamflow in the Connecticut River Basin, which is located in the Eastern United States. Results indicate that winter streamflow over a period of 64 years (1950-2013) lies outside the range expected from natural variability of climate alone with a 90% confidence interval in the climate models. Investigation of relative uncertainties shows that the uncertainty linked to the climate data is greater than the uncertainty induced by hydrologic modeling. Detection uncertainty, defined as the uncertainty related to time evolution of the anthropogenic climate change in the historical data (signal) above the natural internal climate variability (noise), shows that uncertainties in natural internal climate variability (piControl) scenarios may be the source of the significant degree of uncertainty in the regional Detection and Attribution study.
... Here, a fingerprint can be defined as a low or single dimension series that plays a principal role in highdimensional climate time series. According to Santer et al. (1995), a fingerprint applied to 'historical' scenarios is the direction in which a signal induced by a historical climate condition is expected to lie. Just as in the signal strength (Eq. ...
... Using the above methodology, the trend of noise time can have multiple outcomes. Lastly, the magnitude of noise (m) can be estimated using the multiple results obtained by Eq. (5) (Santer et al., 1995). ...
... The signal to noise ratio (SNR) is examined by using the winter streamflow obtained from the median values (most likely values) of the posterior parameter set. It may be more reasonable that its own 'piControl' scenario of the GCM is employed instead of the pooled 'piControl' scenario to reflect the characteristics of each model (Santer et al., 1995). SNR results for the Connecticut River Basin are shown in Fig. 10. ...
In spite of recent popularity for investigating human-induced climate change in regional areas, understanding the contributors to the relative uncertainties in the process remains unclear. To remedy this, this study presents a statistical framework to quantify relative uncertainties in a detection and attribution study. Primary uncertainty contributors are categorized into three types: climate data, hydrologic, and detection uncertainties. While an ensemble of climate models is used to define climate data uncertainty, hydrologic uncertainty is defined using a Bayesian approach. Before relative uncertainties in the detection and attribution study are quantified, an optimal fingerprint-based detection and attribution analysis is employed to investigate changes in winter streamflow in the Connecticut River Basin, which is located in the Eastern United States. Results indicate that winter streamflow over a period of 64 years (1950–2013) lies outside the range expected from natural variability of climate alone with a 90% confidence interval in the climate models. Investigation of relative uncertainties shows that the uncertainty linked to the climate data is greater than the uncertainty induced by hydrologic modeling. Detection uncertainty, defined as the uncertainty related to time evolution of the anthropogenic climate change in the historical data (signal) above the natural internal climate variability (noise), shows that uncertainties in natural internal climate variability (piControl) scenarios may be the source of the significant degree of uncertainty in the regional Detection and Attribution study.
... Moreover, many of the robustly simulated cloud responses to external forcing can also arise as a result of natural climate variability alone, with the magnitude of the unforced response relative to the forced response increasing as shorter time scales are considered. Nevertheless, models project robust climate changes that are likely to affect large-scale cloud properties: poleward shifts of key atmospheric circulation features (Yin 2005), an intensification of the hydrological cycle (Held and Soden 2006), and changes to the vertical temperature structure (Santer et al. 2003(Santer et al. , 2013. In this study we use these properties to perform the first formal detection and attribution study on observed cloud trends. ...
... In the parlance of climate change detection and attribution (D&A), the ''fingerprint'' is the spatial pattern that characterizes the climate system response to external forcing (see, e.g., Allen and Stott 2003;Gillett et al. 2002;Hegerl et al. 1996;Stott et al. 2000;Tett et al. 2002). In this study, we use techniques developed and refined by, for instance, Barnett et al. (2008) and Santer et al. (1995Santer et al. ( , 2013. We calculate this pattern using the externally forced ALL18.5 simulations spanning the period 1900-2100. ...
... 5) The multivariate climate change fingerprint (Santer et al. 1995) is then defined as the eigenfunction of M T M corresponding to the largest eigenvalue (i.e., the leading EOF of M). ...
Understanding the cloud response to external forcing is a major challenge for climate science. This crucial goal is complicated by intermodel differences in simulating present and future cloud cover and by observational uncertainty. This is the first formal detection and attribution study of cloud changes over the satellite era. Presented herein areCMIP5 model-derived fingerprints of externally forced changes to three cloud properties: the latitudes at which the zonally averaged total cloud fraction (CLT) is maximized or minimized, the zonal average CLT at these latitudes, and the height of high clouds at these latitudes. By considering simultaneous changes in all three properties, the authors define a coherent multivariate fingerprint of cloud response to external forcing and use models from phase 5 of CMIP (CMIP5) to calculate the average time to detect these changes. It is found that given perfect satellite cloud observations beginning in 1983, the models indicate that a detectable multivariate signal should have already emerged. A search is then made for signals of external forcing in two observational datasets: ISCCP andPATMOS-x. The datasets are both found to show a poleward migration of the zonal CLT pattern that is incompatible with forcedCMIP5 models.Nevertheless, a detectable multivariate signal is predicted by models over the PATMOS-x time period and is indeed present in the dataset. Despite persistent observational uncertainties, these results present a strong case for continued efforts to improve these existing satellite observations, in addition to planning for new missions.
... In this previous "single variable" work, each variable is typically considered individually. Relatively few studies examine whether detection of a multivariate fingerprintin which different climate variables are combinedyields earlier detection of human influence than in "single-variable" cases (Santer et al. 1995;Pierce et al. 2012;Bonfils et al. 2020). ...
... Previous work has shown that combining physically connected variables into a single fingerprint can help to reduce uncertainty in detection and attribution studies and may yield earlier fingerprint detection times (Santer et al. 1995;Pierce et al. 2012;Bonfils et al. 2020). ...
Human influence has been robustly detected throughout many parts of the climate system. Pattern-based methods have been used extensively to estimate the strength of model-predicted “fingerprints,” both human and natural, in observational data. However, individual studies using different analysis methods and time periods yield inconsistent estimates of the magnitude of the influence of anthropogenic aerosols, depending on whether they examined the troposphere, surface, or ocean. Reducing the uncertainty of the impact of aerosols on the climate system is crucial for understanding past climate change and obtaining more reliable estimates of climate sensitivity. To reconcile divergent estimates of aerosol effects obtained in previous studies, we apply the same regression-based detection and attribution method to three different variables: mid-to-upper-tropospheric temperature, surface temperature, and ocean heat content. We find that quantitative estimates of human influence in observations are consistent across these three independently monitored components of the climate system. Combining the troposphere, surface, and ocean data into a single multivariate fingerprint results in a small (∼10%) reduction of uncertainty of the magnitude of the greenhouse gas fingerprint, but a large (∼40%) reduction for the anthropogenic aerosol fingerprint. This reduction in uncertainty results in a substantially earlier time of detection of the multivariate aerosol fingerprint when compared to aerosol fingerprint detection time in each of the three individual variables. Our results highlight the benefits of analyzing data across the troposphere, surface, and ocean in detection and attribution studies, and motivate future work to further constrain uncertainties in aerosol effects on climate.
Significance Statement
Fingerprints of human influence have been detected separately across the troposphere, surface, and ocean. Previous studies examining the different parts of the climate system are difficult to compare quantitatively, however, because they use different methods and cover differ timespans. Here we find consistent estimates of the human influence on the troposphere, surface, and ocean over recent decades when the same fingerprint method and analysis period is used. When we combine the three variables into a single fingerprint, the uncertainty of the influence of anthropogenic aerosols is substantially reduced and the signal is detectable considerably earlier in the observational record. Our results highlight the benefits of performing analysis across different variables instead of focusing on one variable only.
... least-squares regression, optimal fingerprints with or without the need for empirical orthogonal function (EOF) truncation and others methods [36][37][38]) . These may be trends, as discussed above, characteristic time series [39], or spatial patterns that capture the forced response [40]. Here, we will treat the 'fingerprint' as a spatial pattern and define the fingerprint of a particular external forcing or collection of forcings as the leading EOF of the average of model simulations run subject to those forcings [41]. ...
... The regional precipitation response to external forcing occurs against a backdrop of natural internal variability: climate "noise". Because we have no recent observations of unforced climate, and because paleoclimate proxies represent a climate forced by preindustrial anthropogenic and natural forcings, we must rely on climate model pre-industrial control simulations (piControl) to characterize this variability [40]. We therefore calculate R1 and PRMEAN for the east and west Sahel in CMIP5 preindustrial control simulations, compute the anomalies, and concatenate the resulting time series. ...
Over the 20th and 21st centuries, both anthropogenic greenhouse gas increases and changes in anthropogenic aerosols have affected rainfall in the Sahel. Using multiple characteristics of Sahel precipitation, we construct a multivariate fingerprint that allows us to distinguish between the model-predicted responses to greenhouse gases and anthropogenic aerosols. Models project the emergence of a detectable signal of aerosol forcing in the middle of the 20th century and a detectable signal of greenhouse gas forcing at the beginning of the 21st. However, the signals of both aerosol and greenhouse gas forcing in observations emerge earlier and are stronger than in the models, far stronger in the case of aerosols. The similarity between the response to aerosol forcing and the leading mode of internal variability makes it difficult to attribute this model-observation discrepancy to errors in the forcing, errors in the forced response, model inability to capture the amplitude of internal variability, or some combination of these. For greenhouse gases, however, the forced response is distinct from internal variability as estimated by models, and the observations are largely commensurate with the model projections.
... The fingerprint is often defined as the leading EOF (i.e. the unit-norm eigenvector 1 v corresponding with the highest eigenvalue 1 ) by computing the eigenvalues and eigenvectors of a spatiotemporal covariance matrix. It often results from first averaging over members of each CMIP5 historical model ensemble and then over models (Hasselmann, 1993, Marvel et al., 2019, Santer et al., 1995. The leading EOF can not only estimate a "form" of response how the variable of interest responds to an external forcing but also reduces the dimensionality of the spatiotemporal data by projecting it onto the fingerprint. ...
... However, GCMs may not simulate climate variability accurately (Johnson et al., 2011), casting doubt over the estimates of C. Even with good estimates of C from GCMs control simulations, studies (Wan et al., 2015, Santer et al., 1995 have showed that optimal approaches have no clear advantages over non-optimal methods. Hence, in this study, we assess the sensitivity of D&A approaches to model uncertainty and bias using a simple least squares approach. ...
Detection of systematic changes in the climate system resulting from anthropogenic forcing is a critical area of research. Detection and attribution of hydro-climatological change has been limited by model uncertainty and bias as well as the poor spatial-temporal coverage of observational data. This study assesses a routinely adopted detection methodology and its sensitivity to model uncertainty and bias within a hydro-climatological context. Using a synthetic case study, we establish the sensitivity of detection approaches to the magnitude and consistency of trend and variance along with the length of data available. It is found that the extent of uncertainty (as measured by the variance) plays a critical role in changing the detection outcome. Another important factor is the consistency of trend between simulations and observations. A case study of soil moisture in select locations within Australia shows that averaging over multiple years (e.g., five years to a decade) improves the detection of the climate change signal as long as consistency in the trends exists. Our results also demonstrate that there are substantial differences in simulated trends across climate models. Therefore, even though ensemble averaging is effective in modulating variance, it has the risk of canceling out the signal over models with markedly different responses.
... Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | between anthropogenic climate change and natural variability (Santer et al., 1995;Baehr et al., 2008). In a first step, NH temperatures from the control run are sampled for a specific segment length (Santer et al., 1995) estimating linear trends for 2900 years. ...
... Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | between anthropogenic climate change and natural variability (Santer et al., 1995;Baehr et al., 2008). In a first step, NH temperatures from the control run are sampled for a specific segment length (Santer et al., 1995) estimating linear trends for 2900 years. For a given length of the estimation period, the probability density function (pdf) derived from the linear trends represents the variability of the unforced system. ...
A long-standing task in climate research has been to distinguish between anthropogenic climate change and natural climate variability. A prerequisite for fulfilling this task is the understanding of the relative roles of external drivers and internal variability of climate and the carbon cycle. Here, we present the first ensemble simulations over the last 1200 years with a comprehensive Earth system model including a fully interactive carbon cycle. Applying up-to-date reconstructions of external forcing including the recent low-amplitude estimates of solar variations, the ensemble simulations reproduce temperature evolutions consistent with the range of reconstructions. The 20th-century warming trend stands out against all pre-industrial trends within the ensemble. Volcanic eruptions are necessary to explain variations in pre-industrial climate such as the Little Ice Age; yet only the strongest, repeated eruptions lead to cooling trends that stand out against the internal variability across all ensemble members. The simulated atmospheric CO<sub>2</sub> concentrations exhibit a stable carbon cycle over the pre-industrial era with multi-centennial variations somewhat smaller than in the observational records. Early land-cover changes have modulated atmospheric CO<sub>2</sub> concentrations only slightly. We provide a model-based quantification of the sensitivity (termed γ) of the global carbon cycle to temperature for a variety of climate and forcing conditions. The magnitude of γ agrees with a recent statistical assessment based on reconstruction data. We diagnose a distinct dependence of γ on the forcing strength and time-scales involved, thus providing an explanation for the systematic difference in the observational estimates for different segments of the last millennium.
... The Intergovernmental Panel on Climate Change (IPCC) attributed many recently observed changes in Earth's physical and biological systems to climate change (IPCC, 2014a, b). Several modelling and observation-based studies show that contemporary climate change has already affected plant phenology (Angert et al., 2005;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006), range and distribution of species (Kelly and Goulden, 2008;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006;Post et al., 2009), species extinction (Parmesan, 2006), phytoplankton (Montes-Hugo et al., 2009), ocean variability (Santer et al., 1995;Pierce et al., 2012), forest disturbances (van Mant-gem et al., 2009;Kurz et al., 2008;Westerling et al., 2006), and sea ice (Stroeve et al., 2012;Post et al., 2013). These studies (e.g., Kelly and Goulden, 2008;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006;Post et al., 2009Post et al., , 2013Montes-Hugo et al., 2009;Stroeve et al., 2012;Pierce et al., 2012;Santer et al., 1995) provide compelling scientific evidence for a pronounced impact of recent climate change; however, studies quantitatively attributing the observed impacts in natural systems to relative contributions of anthropogenic forcing and natural variability are rare (e.g., Stone et al., 2013), and differences between models and observations have been well understood but not resolved (Fyfe et al., 2013). ...
... Several modelling and observation-based studies show that contemporary climate change has already affected plant phenology (Angert et al., 2005;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006), range and distribution of species (Kelly and Goulden, 2008;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006;Post et al., 2009), species extinction (Parmesan, 2006), phytoplankton (Montes-Hugo et al., 2009), ocean variability (Santer et al., 1995;Pierce et al., 2012), forest disturbances (van Mant-gem et al., 2009;Kurz et al., 2008;Westerling et al., 2006), and sea ice (Stroeve et al., 2012;Post et al., 2013). These studies (e.g., Kelly and Goulden, 2008;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006;Post et al., 2009Post et al., , 2013Montes-Hugo et al., 2009;Stroeve et al., 2012;Pierce et al., 2012;Santer et al., 1995) provide compelling scientific evidence for a pronounced impact of recent climate change; however, studies quantitatively attributing the observed impacts in natural systems to relative contributions of anthropogenic forcing and natural variability are rare (e.g., Stone et al., 2013), and differences between models and observations have been well understood but not resolved (Fyfe et al., 2013). These differences, caused by a combination of modelling errors in expected differences in internal variabil-Published by Copernicus Publications on behalf of the European Geosciences Union. ...
A lack of long-term measurements across Earth's biological and physical
systems has made observation-based detection and attribution of climate
change impacts to anthropogenic forcing and natural variability difficult.
Here we explore coherence among land, cryosphere and ocean responses to
recent climate change using 3 decades (1980–2012) of observational
satellite and field data throughout the Northern Hemisphere. Our results show
coherent interannual variability among snow cover, spring phenology, solar
radiation, Scandinavian Pattern, and North Atlantic Oscillation. The
interannual variability of the atmospheric peak-to-trough CO2 amplitude
is mostly impacted by temperature-mediated effects of El Niño/Southern
Oscillation (ENSO) and Pacific/North American Pattern (PNA), whereas CO2
concentration is affected by Polar Pattern control on sea ice extent
dynamics. This is assuming the trend in anthropogenic CO2 emission
remains constant, or the interannual changes in the trends are negligible.
Our analysis suggests that sea ice decline-related CO2 release may
outweigh increased CO2 uptake through longer growing seasons and higher
temperatures. The direct effects of variation in solar radiation and leading
teleconnections, at least in part via their impacts on temperature, dominate
the interannual variability of land, cryosphere and ocean indicators. Our
results reveal a coherent long-term changes in multiple physical and
biological systems that are consistent with anthropogenic forcing of Earth's
climate and inconsistent with natural drivers.
... The Intergovernmental Panel on Climate Change (IPCC) attributed many recently observed changes in Earth's physical and biological systems to climate change (IPCC, 2014a, b). Several modelling and observation-based studies show that contemporary climate change has already affected plant phenology (Angert et al., 2005;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006), range and distribution of species (Kelly and Goulden, 2008;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006;Post et al., 2009), species extinction (Parmesan, 2006), phytoplankton (Montes-Hugo et al., 2009), ocean variability (Santer et al., 1995;Pierce et al., 2012), forest disturbances (van Mant-gem et al., 2009;Kurz et al., 2008;Westerling et al., 2006), and sea ice (Stroeve et al., 2012;Post et al., 2013). These studies (e.g., Kelly and Goulden, 2008;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006;Post et al., 2009Post et al., , 2013Montes-Hugo et al., 2009;Stroeve et al., 2012;Pierce et al., 2012;Santer et al., 1995) provide compelling scientific evidence for a pronounced impact of recent climate change; however, studies quantitatively attributing the observed impacts in natural systems to relative contributions of anthropogenic forcing and natural variability are rare (e.g., Stone et al., 2013), and differences between models and observations have been well understood but not resolved (Fyfe et al., 2013). ...
... Several modelling and observation-based studies show that contemporary climate change has already affected plant phenology (Angert et al., 2005;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006), range and distribution of species (Kelly and Goulden, 2008;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006;Post et al., 2009), species extinction (Parmesan, 2006), phytoplankton (Montes-Hugo et al., 2009), ocean variability (Santer et al., 1995;Pierce et al., 2012), forest disturbances (van Mant-gem et al., 2009;Kurz et al., 2008;Westerling et al., 2006), and sea ice (Stroeve et al., 2012;Post et al., 2013). These studies (e.g., Kelly and Goulden, 2008;Parmesan and Yohe, 2003;Walther et al., 2002;Parmesan, 2006;Post et al., 2009Post et al., , 2013Montes-Hugo et al., 2009;Stroeve et al., 2012;Pierce et al., 2012;Santer et al., 1995) provide compelling scientific evidence for a pronounced impact of recent climate change; however, studies quantitatively attributing the observed impacts in natural systems to relative contributions of anthropogenic forcing and natural variability are rare (e.g., Stone et al., 2013), and differences between models and observations have been well understood but not resolved (Fyfe et al., 2013). These differences, caused by a combination of modelling errors in expected differences in internal variabil-Published by Copernicus Publications on behalf of the European Geosciences Union. ...
A lack of long-term measurements across Earth's biological and physical systems has made observation-based detection and attribution of climate change impacts to anthropogenic forcing and natural variability difficult. Here we explore coherence among land, cryosphere and ocean responses to recent climate change using three decades (1980−2012) of observational satellite and field data throughout the Northern Hemisphere. Our results show coherent interannual variability among snow cover, spring phenology and thaw, solar radiation, Scandinavian Pattern, and North Atlantic Oscillation. The interannual variability of the atmospheric peak-to-trough CO2 amplitude is mostly impacted by temperature-mediated effects of ENSO, North American Pattern and East Atlantic Pattern, whereas CO2 concentration is affected by Polar Pattern control on sea ice extent dynamics. This is assuming the trend in anthropogenic CO2 emission remains constant, or the interannual changes in the trends are negligible. Our analysis suggests that sea ice decline-related CO2 release may outweigh increased CO2 uptake through longer growing seasons and higher temperatures. The direct effects of variation in solar radiation and leading teleconnections, at least in part via their impacts on temperature, dominate the interannual variability of land, cryosphere and ocean indicators. Our results reveal a coherent long-term changes in multiple physical and biological systems that are consistent with anthropogenic forcing of Earth's climate and inconsistent with natural drivers.
... Detection and attribution studies have been previously performed to investigate the nature of changes in various climatological variables such as air temperature (Hegerl et al. 1996, Allen andStott 2003), ocean heat content (Barnett et al. 2001), ocean circulation indices (Santer et al. 1995), tropospheric moisture content (Santer et al. 2007), precipitation (Mondal and Mujumdar 2012), and streamflow (Mondal andMujumdar 2012, Barnett et al. 2008). Most of these studies deal with climatological or meteorological variables at the global or continental scale. ...
... Least square linear trends are fitted to segments of Z(t) and non overlapping N(t) time series (Mondal and Mujumdar 2012). According to Santer et al. (1995), for m non overlapping segments in N(t) with slopes β(c), c = 1,2,3.....m, the noise ε is given by (9) Detection is achieved when the signal to noise ratio is greater than the threshold value. As per Barnett et al. (2001), detection is achieved when the observed signal strength along with its 95% confidence interval excludes zero. ...
Scientific studies have yielded evidence to support the common perception that climatic variables and associated natural resources and human systems are being affected by external forcings. Detection and attribution (D&A) of climate change provides a formal tool to decipher the complex causes of climate change. This work aims to statistically detect such climatic change signals, if any, in the monthly precipitation data of the Chaliyar river basin, Kerala, India and to evaluate the factors contributing to it. Data employed for the study includes monthly mean precipitation observations, National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data sets and results of several climate model runs based on natural internal variability and anthropogenic effects. Precipitation data from the GCMs were statistically downscaled to river basin scale using ANN based models and the potential predictors were identified by correlation coefficient analysis.
... Detection normally refers to a demonstration that observed variability in the climate system exceeds the range expected from natural variability at some specified (e.g., 5%) level of significance [5,21]. Detection period refers to the length of a data record required to unequivocally detect an anthropogenic signal, while detection time indicates the point in time at which the signal becomes detectable [35]. The latter is related to the ''time of emergence'', or the point in time at which the anthropogenic signal emerges from the ''historical envelope'' of natural variability, but there are subtle differences between the two. ...
... Modern coupled climate/carbon cycle models provide a homogeneous data set with which to conduct such an experiment, which despite its shortcomings overcomes some of the problems of earlier studies. Some early detection studies were limited by the lack of extended control simulations with models identical to their forced runs, or their control runs were done with ocean-only models for which stochastic forcing had to be employed to generate internal variability [35]. The current data set includes multiple realizations of the forced simulations, and an extended control run (with an identical model) from which the preindustrial variability can be estimated. ...
Changes in ocean chemistry and climate induced by anthropogenic CO2 affect a broad range of ocean biological and biogeochemical processes; these changes are already well underway. Direct effects of CO2 (e.g. on pH) are prominent among these, but climate model simulations with historical greenhouse gas forcing suggest that physical and biological processes only indirectly forced by CO2 (via the effect of atmospheric CO2 on climate) begin to show anthropogenically-induced trends as early as the 1920s. Dates of emergence of a number of representative ocean fields from the envelope of natural variability are calculated for global means and for spatial 'fingerprints' over a number of geographic regions. Emergence dates are consistent among these methods and insensitive to the exact choice of regions, but are generally earlier with more spatial information included. Emergence dates calculated for individual sampling stations are more variable and generally later, but means across stations are generally consistent with global emergence dates. The last sign reversal of linear trends calculated for periods of 20 or 30 years also functions as a diagnostic of emergence, and is generally consistent with other measures. The last sign reversal among 20 year trends is found to be a conservative measure (biased towards later emergence), while for 30 year trends it is found to have an early emergence bias, relative to emergence dates calculated by departure from the preindustrial mean. These results are largely independent of emission scenario, but the latest-emerging fields show a response to mitigation. A significant anthropogenic component of ocean variability has been present throughout the modern era of ocean observation.
... In this paper, we argue that the presence of two physically robust, interlinked mechanisms necessitates the use of multivariate detection techniques (16). We propose a method to simultaneously detect the intensification and latitudinal redistribution of global precipitation, test these changes against model estimates of natural internal variability, and investigate the roles of various relevant external forcings. ...
... We estimate the expected response of the dynamic and thermodynamic indicators to external forcing using a leading "fingerprint" method (16,19). We begin by first averaging the anomaly time series D H ′ ðtÞ and T H ′ ðtÞ over an individual model's spliced historical and RCP8.5 realizations, and then averaging over all models. ...
Significance
This study provides evidence that human activities are affecting precipitation over land and oceans. Anthropogenic increases in greenhouse gases and stratospheric ozone depletion are expected to lead to a latitudinal intensification and redistribution of global precipitation. However, detecting these mechanisms in the observational record is complicated by strong climate noise and model errors. We establish that the changes in land and ocean precipitation predicted by theory are indeed present in the observational record, that these changes are unlikely to arise purely due to natural climate variability, and that external influences, probably anthropogenic in origin, are responsible.
... By Gerbrand Komen after some discussions with Luigi Cavaleri.2 Details and references can be found in Hisashi Mitsuyasu's excellent Historical Note on the Study of Ocean Surface Waves (Journal of Oceanography, 58, pp.[109][110][111][112][113][114][115][116][117][118][119][120] 2002), and also in Klaus' own account[95].3 The Strands of Klaus Hasselmann's Science ...
In 2006, Hans von Storch and Dirk Olbers ran an interview with Klaus Hasselmann.
... By Gerbrand Komen after some discussions with Luigi Cavaleri.2 Details and references can be found in Hisashi Mitsuyasu's excellent Historical Note on the Study of Ocean Surface Waves (Journal of Oceanography, 58, pp.[109][110][111][112][113][114][115][116][117][118][119][120] 2002), and also in Klaus' own account[95].3 The Strands of Klaus Hasselmann's Science ...
Presents the oeuvre of Klaus Hasselmann, one of the leading figures of 20th century climate science
This book is is open access which means you have free and unlimited access
... By Gerbrand Komen after some discussions with Luigi Cavaleri.2 Details and references can be found in Hisashi Mitsuyasu's excellent Historical Note on the Study of Ocean Surface Waves (Journal of Oceanography, 58, pp.[109][110][111][112][113][114][115][116][117][118][119][120] 2002), and also in Klaus' own account[95].3 The Strands of Klaus Hasselmann's Science ...
Klaus Hasselmann was born in Hamburg in 1931. His family fled to England in 1934 because of the Nazis, so he grew up in an English-speaking environment, and returned to Hamburg after the war, where he studied physics, started a family, and became an innovative researcher. Later, he spent several years in the United States of America, but always returned to Hamburg, where he became the founding director of the Max-Planck-Institut für Meteorologie in 1975. His Institute soon became one of the world’s leading research facilities in the field of climate science. He retired in 2000, but continued his work in climate science as a “grey eminence” in the background, whilst his heart and mind turned to particle physics. He recently turned 90, and we—a group of former co-workers, scientific friends and colleagues—decided that we had to tell the story of this remarkable man.
... By Gerbrand Komen after some discussions with Luigi Cavaleri.2 Details and references can be found in Hisashi Mitsuyasu's excellent Historical Note on the Study of Ocean Surface Waves (Journal of Oceanography, 58, pp.[109][110][111][112][113][114][115][116][117][118][119][120] 2002), and also in Klaus' own account[95].3 The Strands of Klaus Hasselmann's Science ...
While Klaus Hasselmann began, as many theoretical physicists do, expecting to find a solution to the “turbulence problem” (whatever that is), he noticed that this would be a rather big challenge, and that it may also be good to tackle easier problems. And that is what he did before returning to the old dream when he retired, although he was not particularly successful in attracting praise and recognition.
... By Gerbrand Komen after some discussions with Luigi Cavaleri.2 Details and references can be found in Hisashi Mitsuyasu's excellent Historical Note on the Study of Ocean Surface Waves (Journal of Oceanography, 58, pp.[109][110][111][112][113][114][115][116][117][118][119][120] 2002), and also in Klaus' own account[95].3 The Strands of Klaus Hasselmann's Science ...
... By Gerbrand Komen after some discussions with Luigi Cavaleri.2 Details and references can be found in Hisashi Mitsuyasu's excellent Historical Note on the Study of Ocean Surface Waves (Journal of Oceanography, 58, pp.[109][110][111][112][113][114][115][116][117][118][119][120] 2002), and also in Klaus' own account[95].3 The Strands of Klaus Hasselmann's Science ...
During his long career, Klaus Hasselmann has been a boss and teacher but also a colleague to many people. Therefore, we have asked quite a few of these people about how they remember their time with him.
... To quantify the possible influence of external forcings on the elevation dependence of the amplitude trend, we use a correlation-based method of detection analyses here, which is applied in many studies (e.g., Qian & Zhang, 2015;Santer et al., 1995;Wan et al., 2015). The series for mean amplitude trend values on non-overlapping altitudinal ranges in observations and model-simulated responses to the abovementioned external forcings are compared by computing correlation coefficients between observations and simulations. ...
Plain Language Summary
The present study documents the variation with altitude of the trend in the seasonal temperature difference over the TP and detects the influence upon it of anthropogenic activities. The seasonal temperature difference weakened in most regions of the TP during 1961–2014. Also, the higher the altitude, the more notable the decrease. The trend in the temperature seasonality over the TP and its variation with altitude can mainly be ascribed to the difference in the rate of warming between winter and summer. The greater warming in winter in higher‐altitude regions over the TP causes the negative tendency of the seasonal temperature difference to amplify with elevation. The model‐simulated responses are able to capture the observed variation with altitude of the trend in the temperature seasonality only if anthropogenic forcing is involved. Moreover, the influence upon it of anthropogenic activities is statistically detectable, with the increase in anthropogenic aerosols being the main contributor. In the model‐simulated response to anthropogenic aerosol–only forcing, the larger decrease in snow‐related albedo at higher altitudes in winter can explain the amplified warming there in winter and thereby the weakening with elevation in the seasonal temperature difference.
... To quantify the significance of projected changes against a backdrop of internal variability, we adopt a commonly used signal-to-noise framework (Santer et al., 1995(Santer et al., , 2011(Santer et al., , 2013. Here, the projected signal in each future scenario is defined to be the best-fit linear trend in the multi-model average annual mean, amplitude, or phase from 2015 to 2100. ...
Future changes to the hydrological cycle are projected in a warming world, and any shifts in drought risk may prove extremely consequential for natural and human systems. In addition to long‐term moistening, drying, or warming trends, perturbations to the annual cycle of regional hydroclimate variables may also have substantial impacts. We analyze projected changes in several hydroclimate variables across the continental United States, along with shifts in the amplitude and phase of their annual cycles. We find that even in regions where no robust change in the annual mean is expected, coherent changes to the annual cycle are projected. In particular, we identify robust regional phase shifts toward earlier arrival of peak evaporation in the northern regions, and peak runoff and total soil moisture in the western regions. Changes in the amplitude of the annual cycle of total and surface soil moisture are also projected, and reflect changes to the annual cycle in surface water supply and demand. Whether changes become detectable above the background noise of internal variability depends strongly on the future scenario considered, and significant changes to the annual cycle are largely avoided in the lowest‐forcing scenario.
... Without knowing the natural behavior of the variable, the significant changes over its natural variability may not be correctly estimated. Santer et al. (1995) considered the patterns of the internal natural behavior of the variable to detect change and change points in reference data. They proposed a signal-to-noise ratio method (SNR), which checks the dissimilarity of climate change signal over internal natural variability (Noise). ...
Ganga-Brahmaputra-Meghna (GBM) river basin is the third-largest and one of the most populated river basins in the world. As climate change is affecting most of the hydrometeorological variables across the globe, this study investigated the existence of climate change signal in all four climatological seasons in the GBM river basin and assessed the contribution of anthropogenic activities, i.e., Greenhouse Gases (GHGs) emission in the change. Significant decreasing trends in the monsoon and a small increase in pre-monsoon precipitation were observed. Negligible change was detected in post-monsoon and winter season precipitation. CMIP5 GCMs were used for climate change detection, change point estimation, and attribution studies. Support Vector Machine (SVM) regression method was adopted to downscale GCM variables at the local scale. Monte-Carlo simulation approach was used to detect changes in different seasons. The climate change ‘signals’ were detectable after the year 1980 using Signal to Noise ratio (SNR) method in the majority of central and north-western regions. The change point was detectable only in annual monsoon precipitation at the basin level. Attribution analysis indicated >50% contribution of anthropogenic activities (GHGs) to annual monsoon precipitation changes. So, there is high confidence that monsoon precipitation in GBM has significantly changed due to anthropogenic activities. Different mitigation and adaption measures are also suggested, which may be adopted to manage the growing demand and water availability in the basin.
... Previous studies have largely focused on time of emergence of an anthropogenically forced AMOC decline; that is, when a trend falls outside the range of its natural variability (Baehr et al., , 2008Jackson & Wood, 2020;Keller, Deutsch, et al., 2007;Keller, Kim, et al., 2007;Roberts & Palmer, 2012;Roberts et al., 2014;Santer et al., 1995;Vellinga & Wood, 2004;Williams et al., 2015). For example, Roberts et al. (2014) estimate that the −0.53 Sv yr −1 AMOC trend, assessed from the first 8 years of RAPID, takes 18 years to be significantly different from the internal variability found in the CMIP5 preindustrial control simulations. ...
Plain Language Summary
There are ongoing discussions in the scientific community about whether the Atlantic Meridional Overturning Circulation transport is slowing down. This is of interest due to the importance of this circulation in transporting heat from the tropics to the northern latitudes. A consensus about its decline is hard to reach due to the limited direct observational data available; with the longest continuous data being 14 years long from 2004 to 2018. We therefore conduct a simulation experiment to examine how many years of data are required to detect a decline in the circulation. We create simulations of the North Atlantic transport based on statistical properties from 20 general circulation models with future climate change projections (until 2100) and from the RAPID array observations (since 2004). Our results demonstrate that the length of data we currently have from observations has just entered the “detection window” of 14–42 years (based on model simulations). However, the RAPID observations do not currently exhibit a statistically significant trend.
... The fingerprint approach, which defines the direction in which the human-induced signal is expected to lie (Santer et al., 1995), reduces the detection problem to a low-dimensional problemin the detection variable (Hegerl, et al., 1996). The two broad approaches for fingerprint-based D&A analysis are the optimal regression-based and pattern correlation based approaches . ...
... I followed up this work with somewhat more realistic but still relatively simple economic models based on non-equilibriium multiagent dynamics. A few nice PhDs theses came out of this, by Volker Barth, Michael Weber and Georg Hooss [150,155]. As a side product, we created a climate computer game based on our coupled climate-economic model that was implemented in a climate exhibition for a year or so at the German Science Museum in Munich. ...
Interviews with five significant scientists, all in English
... Climate change is taking place and it's also visible in observed data of temperature, precipitation, sea level etc. Research community is trying to detect the effect of climate change in different areas i.e. Ocean circulation indices (Santer et al. 1995), snow and high elevation sites [3,4,5], temperature [6,7,8], precipitation [9, 10, Proceedings of IOE Graduate Conference, 2015 Figure 1: CMIP3 Emission scenarios (Source: https://www.agclimate.net/scenario-planning-series-part-2-bring-onthe-acronyms-a-brief-overview-of-ipcc-scenarios) 11] and streamflow [12,13,14] etc. ...
Almost all aspects of climatic patterns are affected by rising level of Greenhouse gases (GHGs) and increasing anthropogenic activities. Change in climate is observed and studied by various researchers. In this article, the present and future effects of climate change on precipitation, temperature, flood events and droughts are discussed in the form of case studies. Significant rising trends in observed temperature are found in some parts Ganga basin. Future trends of temperature and precipitation also shows rising trend in entire Ganga basin. Rajasthan, a semi-arid hot zone, receives lesser rainfall and more prone to droughts. Historical and future trends of Standard precipitation index (SPI), which is a drought index, are also discussed in this study. It is seen that, overall there will be less severe droughts based on annual and monsoon months, but Northern and Western part of Rajasthan will be prone to more sever droughts. Rising level of CO 2 emission is major contributor to the global warming. To keep the global warming lesser than 2 • C than pre-industrial time, carbon capture and storage (CCS) is only feasible solution. Maintaining the sustainability of the water resources is of prime importance because (i) due to the rising temperature, the available water resources will decline in the long term and (ii) water requirements will increase due to the growing population and economic advancements. This article also discusses the considerations and components of sustainable water resource management highlighting the approaches employed for managing agricultural water which is a major shareholder in the consumption of water resources.
... Here, we use the choice of m (=15) to rotate away from high noise directions and obtain the optimized fingerprint F * . Furthermore, to reduce the artificial skill, the same noise data C 1 (t) or C 2 (t) is never contemporaneously used to optimize the fingerprint and estimate the signal-free time series N(t) (Santer et al. 1995). Full details of the detection method are as follows. ...
... We compare observations with model-simulated responses to ALL, ANT, and NAT to identify observational evidence of climate responses to anthropogenic forcing and natural external forcing. We use two different methods (described below) to test robustness of the results: one is a correlation-based nonoptimal method (Santer et al. 1995;Wan et al. 2015), and the other is the optimal fingerprinting approach (Allen and Stott 2003) with a regularized covariance estimate (Ribes et al. 2009(Ribes et al. , 2013. ...
The annual cycle is the largest variability for many climate variables outside the tropics. Whether human activities have affected the annual cycle at the regional scale is unclear. In this study, long-term changes in the amplitude of surface air temperature annual cycle in the observations are compared with those simulated by the climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Different spatial domains ranging from hemispheric to subcontinental scales in mid- to high-latitude land areas for the period 1950–2005 are considered. Both the optimal fingerprinting and a nonoptimal detection and attribution technique are used. The results show that the space–time pattern of model-simulated responses to the combined effect of anthropogenic and natural forcings is consistent with the observed changes. In particular, models capture not only the decrease in the temperature seasonality in the northern high latitudes and East Asia, but also the increase in the Mediterranean region. A human influence on the weakening in the temperature seasonality in the Northern Hemisphere is detected, particularly in the high latitudes (50–70N) where the influence of the anthropogenic forcing can be separated from that of the natural forcing.
... Here, we use the choice of m (=15) to rotate away from high noise directions and obtain the optimized fingerprint F * . Furthermore, to reduce the artificial skill, the same noise data C 1 (t) or C 2 (t) is never contemporaneously used to optimize the fingerprint and estimate the signal-free time series N(t) (Santer et al. 1995). Full details of the detection method are as follows. ...
The characteristics and causes of inhomogeneous warming of the Tropical Indian Ocean (TIO) sea surface temperature during 1900-2005 are investigated based on observations and 16 Coupled Model Intercomparison Project phase 5 (CMIP5) models. Over the TIO, the observed warming trend has more than doubled since 1965, which is well simulated by the CMIP5 historical runs. However, as to spatial warming pattern, observations manifest a double-peak pattern during 1900-1940 and a non-uniform Indian Ocean Mode (IOBM)-like pattern during 1965-2005, which is not captured by the CMIP5 historical runs. Herein, an optimal detection analysis is employed, which indicates that the double-peak warming pattern can be explained well by a combination of Greenhouse Gas (GHG) and natural forcing, and the non-uniform IOBM-like pattern is mostly attributable to anthropogenic forcing. Further, a mixed-layer heat budget analysis shows that atmospheric and oceanic processes, especially latent heat flux from atmospheric forcing part associated with GHG forcing, are beneficial for the warming patterns formation. Our study supports the claim that intrinsic ocean-atmosphere interaction within the TIO is the key mechanism for maintaining the TIO warming. From the model perspective, during 1900-1940, the weak anti-symmetric atmospheric circulation with easterly (northwesterly) anomalies north (south) of the equator helps to sustain the double-peak warming pattern. During 1965-2005, the intensified anti-symmetric wind pattern is in favor of the non-uniform IOBM-like warming pattern.
... Nevertheless, it provides an easy-to-understand view of the similarity between observed and model-simulated changes. Santer et al. (1995) used a correlation-based method to conduct detection and attribution analysis for temperature changes. ...
Using an optimal fingerprinting method and improved observations, we compare observed and CMIP5 model simulated annual, cold season and warm season (semi-annual) precipitation over northern high-latitude (north of 50°N) land over 1966–2005. We find that the multi-model simulated responses to the effect of anthropogenic forcing or the effect of anthropogenic and natural forcing combined are consistent with observed changes. We also find that the influence of anthropogenic forcing may be separately detected from that of natural forcings, though the effect of natural forcing cannot be robustly detected. This study confirms our early finding that anthropogenic influence in high-latitude precipitation is detectable. However, in contrast with the previous study, the evidence now indicates that the models do not underestimated observed changes. The difference in the latter aspect is most likely due to improvement in the spatial–temporal coverage of the data used in this study, as well as the details of data processing procedures.
... Vogel (1986) extended this approach to lognormal and Gumbel distributions for the goodness-of-fit test and GEV (Chowdhury et al. 1991). The CD has been popularly used to assess climate change detection (Santer et al. 1995(Santer et al. , 1996 and hydroclimatological applications (Willmott et al. 1985). This measurement, however, only evaluates linear relationships between the variables. ...
In the frequency analyses of extreme hydrometeorological events, the restriction of statistical independence and identical distribution (iid) from year to year ensures that all observations are from the same population. In recent decades, the iid assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, the objective of the current study is to propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.
... We conducted a formal detection analysis to determine at which point in time a forced climate change signal significantly deviates from the unforced natural variability. We applied a fingerprint approach, similar to one used, for example, by Baehr et al. (2008Baehr et al. ( , 2007, Hasselmann (1997), Andrews et al. (2013), and Santer et al. (1995) to model simulations. The climate variability is approximated by linear trends using a least squares fit to time series of wind speed and significant wave and swell heights. ...
Surface waves in the ocean respond to variability and changes of climate. Observations and modeling studies indicate trends in wave height over the past decades. Nevertheless, it is currently impossible to discern whether these trends are the result of climate variability or change. We used the output of an Earth system model (EC-Earth) produced within the 5th phase of the Coupled Model Intercomparison Project (CMIP5) to force a global wave model (WAM) in order to study the response of waves to different climate regimes. We ran a control simulation to determine the natural (unforced) model variability. Using a simplified fingerprint approach we calculated positive and negative limits of natural variability for wind speed and significant wave height which were then compared to different (forced) climate regimes over the historical period (1850-2010) and in the future climate change scenario RCP8.5 (2010-2100). We found detectable climate change signals in the current decade (2010-2020) in the North Atlantic, Equatorial Pacific and Southern Ocean. Until the year 2060, climate change signals are detectable in 60% of the global ocean area. We show that climate change acts to generate detectable trends in wind speed and significant wave height which exceed the positive and the negative ranges of natural variability in different regions of the ocean. Moreover, in more than 3% of the ocean area, the climate change signal is reversible, such that trends exceeded both positive and negative limits of natural variability in different points in time. We attribute these changes to local (due to local wind) and remote (due to swell) factors.
... Special efforts have been devoted to estimating natural climate variability since the outcome of any statistical significance test crucially depends on the climate noise estimate (Santer et al. 1995). In this study we accounted for the assumption of near stationarity of natural climate noise by applying the ARMA pre-filters to the models of the analysis such that the residuals generated were a white noise process. ...
Human activities such as conversion of natural ecosystem to croplands and urban-centers, deforestation and afforestation impact biophysical properties of land surface such as albedo, energy balance, and surface roughness. Alterations in these properties affect the heat and moisture exchanges between the land surface and atmospheric boundary layer. The objectives of this research were; (i) to quantitatively identify the High plains’ regional climate change in temperatures over the period 1895 to 2006, (ii) detect the signatures of anthropogenic forcing of LULC changes on the regional climate change of the High Plains, and (iii) examine the trends in evolving regional latent heat flux under the changing climate during the past thirty years.
We investigated the regional climate change by comparing two trend periods, the reference period (1895 – 1930) and the warming period (1971 – 2006), using the base period as 1935 – 1965. For the objective (ii) the study developed an enhanced signal processing procedure to maximize the signal to noise ratio by introducing a pre-filtering technique of ARMA modeling, before applying the optimal fingerprinting technique to detect the signals of LULC change. For the objective (iii), we estimated ETc using the widely accepted two-step approach. We developed a linear model to estimate spatial crop coefficient (Kc) from AVHRR-based NDVI. The Kc estimates were used to adjust spatial ETo estimates, thereby yielding spatial ETc estimates that are representative of the summer latent heat fluxes of years 1981 to 2006.
The results from the study show that, the overall warming trend in the High Plains was about 0.11oC/decade. The minimum temperature had the strongest warming at a rate of 0.19oC/decade. Due to LULC changes attributed to increase in irrigation application and vegetation surfaces, more surface energy in summer is being redistributed into latent heat flux. Therefore, there is a significant influence of evaporative cooling on regional temperatures during summer season. As a result, the greenhouse warming effect in the region is being surpassed.
... In the absence of long observational AMOC records, it will remain difficult to disentangle forced responses from internal climate variability using a single time series. More sophisticated detection and attribution techniques may allow earlier separation of signal and noise by exploiting differences in the spatial patterns of internal variability and the response to external forcings [Santer et al., 1995]. The combination of continued observation of the AMOC at 26.5 • N, continued monitoring of water mass properties in the subpolar gyre, and proposed arrays to monitor the AMOC at additional latitudes [Srokosz et al., 2012] would constitute a powerful system for detecting changes to the Atlantic in the future and will be essential for better understanding of the magnitudes and patterns of AMOC variability on a range of time scales. ...
The Atlantic meridional overturning circulation (AMOC) at 26.5°N weakened by −0.53 Sv/yr between April 2004 and October 2012. To assess whether this trend is consistent with the expected ‘noise’ in the climate system, we compare the observed trend with estimates of internal variability derived from 14 control simulations from the Climate Model Intercomparison Project 5 (CMIP5). Eight-year trends of −0.53 Sv/yr are relatively common in two models but are extremely unusual (or out of range) in the other twelve. However, all 14 models underestimate AMOC variability on interannual time scales. To account for this bias, we estimate plausible upper-limits of internal AMOC variability by combining the temporal correlation characteristics of the AMOC from CMIP5 models with an observational estimate of interannual variability. We conclude that the observed AMOC trend is not significantly different (p > 0.01) from plausible estimates of internal variability. Detecting the influence of external climate forcings on the AMOC will require more than one decade of continuous observations.
... I followed up this work with somewhat more realistic but still relatively simple economic models based on non-equilibriium multi-agent dynamics. A few nice PhDs theses came out of this, by Volker Barth, Michael Weber and Georg Hooss [150,155]. As a side product, we created a climate computer game based on our coupled climate-economic model that was implemented in a climate exhibition for a year or so at the German Science Museum in Munich. ...
... This approach is useful not only for the early detection of externally forced climate change, but also to distinguish between different candidate forcing mechanisms in the attribution problem (Ha97). The optimal fingerprint method has been applied by Santer et al. (1995b) for the detection of ocean global warming in a model simulation study and by Hetal96 for the detection of a greenhouse warming signal in near surface temperature data. It can be shown that the optimal fingerprint method is closely related to other optimal averaging or filtering methods (Bell 1986;see Hegerl and North 1997) which provide an optimal estimate of the amplitude of a climate change signal in the presence of noise, and to similar approaches which have been used in other fields of signal processing (see Hasselmann 1979;Allen and Tett 1997). ...
Bringing together many of the world's leading experts, this volume is a comprehensive, state-of-the-art review of climate change science, impacts, mitigation, adaptation, and policy. It provides an integrated assessment of research on the key topics that underlie current controversial policy questions. The first part of the book addresses recent topics and findings related to the physical-biological earth system. The next part of the book surveys estimates of the impacts of climate change for different sectors and regions. The third part examines current topics related to mitigation of greenhouse gases and explores the potential roles of various technological options. The last part focuses on policy design under uncertainty. Dealing with the scientific, economic and policy questions at the forefront of the climate change issue, this book will be invaluable for graduate students, researchers and policymakers interested in all aspects of climate change and the issues that surround it.
How do ocean initial states impact historical and future climate projections in Earth system models? To answer this question, we use the 50-member Canadian Earth System Model (CanESM2) large ensemble, in which individual ensemble members are initialized using a combination of different oceanic initial states and atmospheric micro-perturbations. We show that global ocean heat content anomalies associated with the different ocean initial states, particularly differences in deep ocean heat content due to ocean drift, persist from initialization at year 1950 through the end of the simulations at year 2100. We also find that these anomalies most readily impact surface climate over the Southern Ocean. Differences in ocean initial states affect Southern Ocean surface climate because persistent deep ocean temperature anomalies upwell along sloping isopycnal surfaces that delineate neighboring branches of the Upper and Lower Cells of the Global Meridional Overturning Circulation. As a result, up to a quarter of the ensemble variance in Southern Ocean turbulent heat fluxes, heat uptake, and surface temperature trends can be traced to variance in the ocean initial state, notably deep ocean temperature differences of order 0.1K due to model drift. Such a discernible impact of varying ocean initial conditions on ensemble variance over the Southern Ocean is evident throughout the full 150 simulation years of the ensemble, even though upper ocean temperature anomalies due to varying ocean initial conditions rapidly dissipate over the first two decades of model integration over much of the rest of the globe.
The last millennium (LM, 1000–1850 AD) is crucial for studying historical climate change on decadal to multidecadal timescales. The summer surface air temperature (SAT) evolutions on regional scales (e.g. over China) are more uncertain than the globe/Northern Hemisphere, especially in response to external forcing factors and internal climate variability. Here, we provide one‐signal (full‐forcing) fingerprints of summer SAT in China derived from three large ensemble model archives with a multi‐proxy reconstruction during the LM, Little Ice Age (LIA, 1451–1850 AD), and Medieval Climate Anomaly (MCA, 1000–1250 AD), respectively. Our results show that (a) SATs in the northeast, southeast, northwest, and Tibetan Plateau (TP) regions of China show evident decreasing trends during the LM. External forcing response from all model archives agrees with the regional SAT reconstruction but underestimates variability in northwest China at the multidecadal timescale. (b) During the LIA, the summer regional SAT exhibits a cold condition in the reconstruction and simulations, especially in the northeast and northwest regions of China. External forcing responses in most model archives are the dominant factor on multidecadal SAT evolutions in the southeast, northeast, and TP regions of China and decadal SAT evolutions in northwest China. (c) During the MCA, detection and attribution of SAT shows that internal climate variability dominates in southeast, northeast, and TP regions of China, but external forcing dominates in northwest China at decadal to multidecadal timescales. These results contribute to a better understanding of the causes and mechanisms of regional climate change.
The Nobel Prize of 2021 highlighted the importance of understanding the complex dynamical processes that govern the evolution of Earth’s climate. Two of the Nobel laureates, Syukuro Manabe and Klaus Hasselmann pioneered the creation of a robust theoretical and mathematical framework for using a hierarchy of models of varying complexity to study a variety of questions, the most important of which may be: how do we quantify the effects of human activities on the Earth’s climate? Three main contributions of Manabe and Hasselmann on which this article focuses are: (i) simple radiative-convective models that study, among other factors, the effect of changes of CO2 concentration; (ii) a methodology to derive simpler, stochastic climate models from more complex, coupled models for the weather; and (iii) mathematical techniques called fingerprinting that quantify the human impact on the climate.
Despite the pervasive impact of drought on human and natural systems, the large-scale mechanisms conducive to regional drying remain poorly understood. Here we use a multivariate approach1,2 to identify two distinct externally forced fingerprints from multiple ensembles of Earth system model simulations. The leading fingerprint, FM1(x), is characterized by global warming, intensified wet–dry patterns3 and progressive large-scale continental aridification, largely driven by multidecadal increases in greenhouse gas (GHG) emissions. The second fingerprint, FM2(x), captures a pronounced interhemispheric temperature contrast4,5, associated meridional shifts in the intertropical convergence zone6–9 and correlated anomalies in precipitation and aridity over California10, the Sahel11,12 and India. FM2(x) exhibits nonlinear temporal behaviour: the intertropical convergence zone moves southwards before 1975 in response to increases in hemispherically asymmetric sulfate aerosol emissions, and it shifts northwards after 1975 due to reduced sulfur dioxide emissions and the GHG-induced warming of Northern Hemisphere landmasses. Both fingerprints are statistically identifiable in observations of joint changes in temperature, rainfall and aridity during 1950–2014. We show that the reliable simulation of these changes requires combined forcing by GHGs, direct and indirect effects of aerosols, and large volcanic eruptions. Our results suggest that GHG-induced aridification may be modulated regionally by future reductions in sulfate aerosol emissions. Large-scale mechanisms causing regional drying are not well understood. Models and observational data reveal that human-caused changes in GHGs and aerosols led to detectable global and hemispheric signals in the joint behaviour of precipitation, temperature and aridity since the 1950s.
This study introduces a simple analytic expression for calculating the lead time required for a linear trend to emerge in a Gaussian first-order autoregressive process. The expression is derived from the standard error of the regression and is tested using the NCAR Community Earth System Model Large Ensemble of climate change simulations. It is shown to provide a robust estimate of the point in time when the forced signal of climate change has emerged from the natural variability of the climate system with a predetermined level of statistical confidence. The expression provides a novel analytic tool for estimating the time of emergence of anthropogenic climate change and its associated regional climate impacts from either observed or modeled estimates of natural variability and trends.
The authors of this Article review the current state of the science of attribution of anthropogenic climate change, with particular emphasis on the methodological challenges that are likely to confront any attempt to establish a direct causal link between greenhouse gas emissions and specific damaging weather events. Standard "detection and attribution" analyses, such as those cited by the Intergovernmental Panel on Climate Change (IPCC), are generally sufficient to establish the strength of human influence on large-scale, long-termaverage climate, but fall short of quantifying the role of greenhouse gas emissions in almost any conceivable case of actual harm, since nobody is directly exposed to a change in global average temperature alone. The authors argue that it should be possible to agree on a relatively objective approach to quantifying the role of human influence on climate in cases of actual harm. There are, however, a number of questions to be resolved, including: can we apply the concept of Fraction Attributable Risk, developed for population studies in epidemiology, to the analysis of an unprecedented change in a single system such as the world's climate? Can we rely on computer simulation to address counter-factual questions such as "what would the climate have been like in the absence of twentieth century greenhouse gas emissions, " given that we are working with imperfect simulation models? Due to multiple anthropogenic and natural contributions to changing weather risks, it will always be necessary to apply some kind of principle of ceteris paribus to quantify the role of any particular causal agent, such as greenhouse gas emissions. How is this principle to be applied? These questions are not, in themselves, scientific issues, although how they are to be resolved will have a direct bearing on how and whether climate science can inform specific causal attribution claims. In summary, we need the legal community to ask the scientific community the right questions. It is imperative that these issues be resolved as soon as possible, to avoid having them become entwined in the outcomes of specific cases. Thus, this Article serves as a kind of tutorial, going over some material that many will find familiar in order to place it in the context of attribution.
Detecting and quantifying the presence of human-induced climate change
in regional hydrology is important for studying the impacts of such
changes on the water resources systems as well as for reliable future
projections and policy making for adaptation. In this article a formal
fingerprint-based detection and attribution analysis has been attempted
to study the changes in the observed monsoon precipitation and
streamflow in the rain-fed Mahanadi River Basin in India, considering
the variability across different climate models. This is achieved
through the use of observations, several climate model runs, a principal
component analysis and regression based statistical downscaling
technique, and a Genetic Programming based rainfall-runoff model. It is
found that the decreases in observed hydrological variables across the
second half of the 20th century lie outside the range that is expected
from natural internal variability of climate alone at 95% statistical
confidence level, for most of the climate models considered. For several
climate models, such changes are consistent with those expected from
anthropogenic emissions of greenhouse gases. However, unequivocal
attribution to human-induced climate change cannot be claimed across all
the climate models and uncertainties in our detection procedure, arising
out of various sources including the use of models, cannot be ruled out.
Changes in solar irradiance and volcanic activities are considered as
other plausible natural external causes of climate change. Time
evolution of the anthropogenic climate change "signal" in the
hydrological observations, above the natural internal climate
variability "noise" shows that the detection of the signal is achieved
earlier in streamflow as compared to precipitation for most of the
climate models, suggesting larger impacts of human-induced climate
change on streamflow than precipitation at the river basin scale.
The ocean's salinity field is driven primarily by evaporation,
precipitation, and river discharge, all key elements of the Earth's
hydrological cycle. Observations show the salinity field has been
changing in recent decades. We perform a formal fingerprint-based
detection and attribution analysis of these changes between
1955-2004, 60°S and 60°N, and in the top 700 m of the
water column. We find that observed changes are inconsistent with the
effects of natural climate variability, either internal to the climate
system (such as El Niño and the Pacific Decadal Oscillation) or
external (solar fluctuations and volcanic eruptions). However, the
observed changes are consistent with the changes expected due to human
forcing of the climate system. Joint changes in salinity and temperature
yield a stronger signal of human effects on climate than either salinity
or temperature alone. When examining individual depth levels, observed
salinity changes are unlikely (p < 0.05) to have arisen from natural
causes over the top 125 m of the water column, while temperature changes
(and joint salinity/temperature changes) are distinct from natural
variability over the top 250 m.
Observed changes in temperature and salinity properties on isopycnals across hydrographic sections throughout the Indo-Pacific are compared with the changes modeled by the coupled climate model, HadCM3. Observations show cooling and freshening on isopycnals in midlatitudes, and there is quantitative agreement between modeled and observed water mass changes on five out of six zonal sections. The full Indo-Pacific pattern of change in the climate model is examined and it is discovered that the pattern of cooling and freshening on isopycnals in midlatitudes, with warming on isopycnals at high latitudes, may be thought of as a fingerprint of anthropogenic forcing. The water mass changes are related to changes in the surface fluxes and it is found that surface warming is the dominant factor in producing water mass changes, although changes in the freshwater cycle are important in the formation zone for Antarctic Intermediate Water. The coupled model has a low-amplitude, low-frequency (100-yr period) internal mode related to the anthropogenic fingerprint. Further observations are required to measure the amplitude of the internal mode as well as the anthropogenically forced mode.
We present an observation-based detection variable for the meridional overturning circulation (MOC) at 26N in the Atlantic. The detection variable is derived by projecting a climate signal onto a pattern of natural variability (Hasselmann, 1979; Baehr, 2007). For the MOC, this method so far not been tested with solely relying on observations. Here, we test the method relying existing/ongoing observations of the zonal density gradient at 26N. For the fixed spatial pattern of natural variability, we use the observations available from the hydrographic occupations of the zonal transect in 1957 and 2004. For the climate signal, we use observations from the RAPID/MOCHA array. We find that the method can not only be meaningful applied to the observations, but yields similar results as in the model simulations. This opens the prospective of timely and reliable detection of MOC changes at 26N in the Atlantic based on the currently implemented observing system. It also results in increased confidence in the previous model results, which suggested that the method employed here reduces the time to detect MOC changes by 50 percent compared to the uni-variate analysis of a single MOC timeseries. In addition, the detection variable has the potential to provide the bounds of natural variability of the MOC at 26N at which models - eventually to be used for MOC predictions - could be tested against.
Previous reviews of the greenhouse problem have concluded that the enhanced greenhouse effect has not yet been detected unequivocally in the observational record. However, they have also noted that the global-mean temperature change over the past 100yr is consistent with the greenhouse hypothesis, and that there is no convincing observational evidence to suggest that the model-based range of possible climate sensitivity values is wrong. The purpose of the present review is to reevaluate these conclusions in the light of more recent evidence. -from Authors
A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a {open_quotes}guess pattern{close_quotes} representing the expected time-space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested. This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near surface temperature trends defined for time intervals of 15-30 years. The expected pattern of climate change is derived from a transient simulation with a coupled ocean-atmosphere general circulation model. Global gridded near-surface temperature observations are used to represent the observed climate change. Information on the natural variability needed to establish the statistics of the detection variable is extracted from long control simulations of coupled ocean-atmosphere models and, additionally, from the observations themselves (from which an estimated greenhouse warming signal has been removed). While the model control simulations contain only variability caused by the internal dynamics of the atmosphere-ocean system, the observations additionally contain the response to various external forcings. The resulting estimate of climate noise has large uncertainties but is qualitatively the best the authors can presently offer. 71 refs., 12 figs., 14 tabs.
Four time-dependent Greenhouse warming experiments were performed with the same global coupled atmosphere-ocean model, but with each simulation using initial conditions from different {open_quotes}snapshots{close_quotes} of the control run climate. The radiative forcing - the increase in equivalent CO{sub 2} concentrations from 1985- 2035 specified in the Intergovernmental Panel on Climate Change (IPCC) scenario A - was identical in all four 50-year integrations. This approach to climate change experiments is called the Monte Carlo technique and is analogous to a similar experimental set-up used in the field of extended range weather forecasting. Despite the limitation of a very small sample size, this approach enables the estimation of both a mean response and the {open_quotes}between-experiment{close_quotes} variability, information which is not available from a single integration. The use of multiple realizations provides insights into the stability of the response, both spatially, seasonally and in terms of different climate variables. The results indicate that the time evolution of the global mean warming signal is strongly dependent on the initial of the climate system. The ensemble mean climate change pattern closely resembles that obtained in a 100 year integration performed with the same model. In global mean terms, the climate change signals for near surface temperature, the hydrological cycle and sea level significantly exceed the variability among the members of the ensemble. Due to the high internal variability of the modelled climate system, the estimated detection time of the global mean temperature change signal is uncertain by at least one decade. It is not possible to identify a significant response in the precipitation and soil moisture fields, variables which are spatially noisy and characterized by large variability between the individual integrations. 36 refs., 16 figs., 3 tabs.
This study investigates the response of a global model of the climate to the quadrupling of the CO2 concentration in the atmosphere. The model consists of (1) a general circulation model of the atmosphere, (2) a heat and water balance model of the continents, and (3) a simple mixed layer model of the oceans. It has a global computational domain and realistic geography. For the computation of radiative transfer, the seasonal variation of insolation is imposed at the top of the model atmosphere, and the fixed distribution of cloud cover is prescribed as a function of latitude and of height. It is found that with some exceptions, the model succeeds in reproducing the large-scale characteristics of seasonal and geographical variation of the observed atmospheric temperature. The climatic effect of a CO2 increase is determined by comparing statistical equilibrium states of the model atmosphere with a normal concentration and with a 4 times the normal concentration of CO2 in the air. It is found that the warming of the model atmosphere resulting from CO2 increase has significant seasonal and latitudinal variation. Because of the absence of an albedo feedback mechanism, the warming over the Antarctic continent is somewhat less than the warming in high latitudes of the northern hemisphere. Over the Arctic Ocean and its surroundings, the warming is much larger in winter than summer, thereby reducing the amplitude of seasonal temperature variation. It is concluded that this seasonal asymmetry in the warming results from the reduction in the coverage and thickness of the sea ice. The warming of the model atmosphere results in an enrichment of the moisture content in the air and an increase in the poleward moisture transport. The additional moisture is picked up from the tropical ocean and is brought to high latitudes where both precipitation and runoff increase throughout the year. Further, the time of rapid snowmelt and maximum runoff becomes earlier.
Four time-dependent greenhouse warming experiments were performed with the same global coupled atmosphere-ocean model, but with each simulation using initial conditions from different “snapshots” of the control run climate. The radiative forcing — the increase in equivalent CO2 concentrations from 1985–2035 specified in the Intergovernmental Panel on Climate Change (IPCC) scenario A — was identical in all four 50-year integrations. This approach to climate change experiments is called the Monte Carlo technique and is analogous to a similar experimental set-up used in the field of extended range weather forecasting. Despite the limitation of a very small sample size, this approach enables the estimation of both a mean response and the “between-experiment” variability, information which is not available from a single integration. The use of multiple realizations provides insights into the stability of the response, both spatially, seasonally and in terms of different climate variables. The results indicate that the time evolution of the global mean warming signal is strongly dependent on the initial state of the climate system. While the individual members of the ensemble show considerable variation in the pattern and amplitude of near-surface temperature change after 50 years, the ensemble mean climate change pattern closely resembles that obtained in a 100-year integration performed with the same model. In global mean terms, the climate change signals for near surface temperature, the hydrological cycle and sea level significantly exceed the variability among the members of the ensemble. Due to the high internal variability of the modelled climate system, the estimated detection time of the global mean temperature change signal is uncertain by at least one decade. While the ensemble mean surface temperature and sea level fields show regionally significant responses to greenhouse-gas forcing, it is not possible to identify a significant response in the precipitation and soil moisture fields, variables which are spatially noisy and characterized by large variability between the individual integrations.
POPs are defined as the normal modes of a linear dynamical representation of the data in terms of a first-order autoregressive vector process with residual noise forcing. POPs associated with real eigenvalues represent nonpropagating, nonoscillatory patterns which decay exponentially. POPs associated with complex eigenvalues occur in complex conjugate pairs and can represent standing wave structures (if one pattern is much stronger than the other), propagating waves (if both patterns are periodic and have the same structure except for a quarter-wavelength shift) or, in general, an arbitrary amphidromal oscillation. A frequency-wave number analysis confirms that this 30- to 60-day wave is the most dominant regular oscillation in the tropical GCM troposphere. The phase velocity of the 30- to 60-day wave varies with longitude from 6 m s⁻¹ in the Indonesian area to more than 30 m s⁻¹ over the eastern Pacific, small phase speeds being associated with large amplitudes and high phase speeds with small amplitudes. In the high-amplitude regions, rainfall and upper air velocity potential are in phase, while in the low-amplitude regions, rainfall and velocity potential appear uncorrelated. At the surface a pattern strongly resembling Gill's theoretical response to an equatorial heating source is found, with a trough to the east and two off-equatorial cyclones to the west of the heating. -from Authors
This paper considers the geographical distribution of various second moment statistics from a noise-forced energy balance climate model and compares them to the same fields derived from a 40-year data set. The variable considered is the surface temperature field over the Earth. The energy balance model is a standardized one used in several previous studies which emphasized the geographical distribution of the seasonal cycle. It treats two-dimensional geography explicitly by using a different (uniform) heat capacity over ocean than over land. The study compares the (spatially filtered or ``smoothed'') point variance data with those generated by the model in low-, medium- and high-frequency bands. The study also examines the correlation of surface temperature fluctuations at six representative test points with those of neighboring points. The only adjustable parameter in the study is the strength of the noise forcing. The intercomparisons show that the model produces maps that look remarkably close to those from the data without too much sensitivity to the form of the driving noise.
A fully coupled ocean-atmosphere model is shown to have irregular oscillations of the thermohaline circulation in the North Atlantic Ocean with a time scale of approximately 50 years. The irregular oscillation appears to be driven by density anomalies in the sinking region of the thermohaline circulation (approximately 52°N to 72°N) combined with much smaller density anomalies of opposite sign in the broad, rising region. The spatial pattern of see surface temperature anomalies associated with this irregular oscillation bears an encouraging resemblance to a pattern of observed interdecadal variability in the North Atlantic. The anomalies of sea surface temperature induce model surface air temperature anomalies over the northern North Atlantic, Arctic, and northwestern Europe.
This study investigates the response of a climate model to a gradual increase or decrease of atmospheric carbon dioxide. The model is a general circulation model of the coupled atmosphere-ocean-land surface system with global geography and seasonal variation of insulation. To offset the bias of the coupled model toward settling into an unrealistic state, the fluxes of heat and water at the ocean-atmosphere interface are adjusted by amounts that vary with season and geography but do not change from one year to the next. Starting from a quasi-equilibrium climate, three numerical time integrations of the coupled model are performed with gradually increasing, constant, and gradually decreasing concentration of atmospheric carbon dioxide.It is noted that the simulated response of sea surface temperature is very slow over the northern North Atlantic and the Circumpolar Ocean of the Southern Hemisphere where vertical mixing of water penetrates very deeply. However, in most of the Northern Hemisphere and low latitudes of the Southern Hemisphere, the distribution of the change in surface air temperature of the model at the time of doubling (or halving) of atmospheric carbon dioxide resembles the equilibrium response of an atmospheric-mixed layer ocean model to CO2 doubling (or halving). For example, the rise of annual mean surface air temperature in response to the gradual increase of atmospheric carbon dioxide increases with latitudes in the Northern Hemisphere and is larger over continents than oceans.When the time-dependent response of the model oceans to the increase of atmospheric carbon dioxide is compared with the corresponding response to the CO2, reduction at an identical rate, the penetration of the cold anomaly in the latter case is significantly deeper than that of the warm anomaly in the former case. The lack of symmetry in the penetration depth of a thermal anomaly between the two cases is associated with the difference in static stability, which is due mainly to the change in the vertical distribution of salinity in high latitudes and temperature changes in middle and low latitudes.Despite the difference in penetration depth and accordingly, the effective thermal inertia of the oceans between the two experiments, the time-dependent response of the global mean surface air temperature in the CO2 reduction experiment is similar in magnitude to the corresponding response in the CO2 growth experiment. In the former experiment with a colder climate, snow and sea ice with high surface albedo cover a much larger area, thereby enhancing their positive feedback effect upon surface air temperature. On the other hand, surface cooling is reduced due to the larger effective thermal inertia of the oceans. Because of the compensation between these two effects, the magnitude of surface air temperature response turned out to be similar between the two experiments.
A stochastic Budyko-Sellers model is considered in which, in contrast to the usual statistical dynamical climate models, the nonaveraged weather fluctuations are retained as internal random forcing terms. Consequently, the climate variables are no longer deterministic but are stochastic variables, which can be characterized by their variance spectra. The calculated variance spectra of the yearly and zonally averaged surface temperature of the earth are consistent with observations both in the qualitative structure of the spectrum and the order of magnitude of the energy levels. DOI: 10.1111/j.2153-3490.1977.tb00749.x
Quantitative frequency-domain and time-domain estimates are made of an important aspect of natural variability of global-mean temperatures, namely, passive internal variability resulting from the modulation of atmospheric variability by the ocean. The results are derived using an upwelling-diffusion, energy-balance climate model. In the frequency domain, analytical spectral results show a transition from a high-frequency region in which the response is determined by the mixed-layer heat capacity and is independent of the climate sensitivity (time scales less than around 10 years), to a low-frequency region in which the response depends only on the climate sensitivity. In the former region the spectral power is proportional to f-2, where f is the frequency, while in the latter the power is independent of frequency. The range of validity of these results depends on the components of the climate system that are included in the model. In this case these restrict the low-frequency results to time scales less than about 1,000 years. A qualitative extrapolation is presented in an attempt to explain the observed low-frequency power spectra from deep-sea-core δ18O time series. The spectral results are also used to estimate the effective heat capacity of the ocean as a function of frequency. At low frequencies, this can range up to 50 times greater than the heat capacity of the mixed layer.
We illustrate some of the general properties of chaotic dissipative dynamical systems with a simple model. One frequently observed property is the existence of extended intervals, longer than any built-in time scale, during which the system exhibits one type of behavior, followed by extended intervals when another type predominates. In models designed to simulate a climate system with no external variability, we find that an interval may persist for decades. We note the consequent difficulty in attributing particular real climatic changes to causes that are not purely internal. We conclude that we cannot say at present, on the basis of observations alone, that a greenhouse-gas-induced global warming has already set in, nor can we say that it has not already set in.
This investigation considers whether observed changes in surface air temperature are consistent with GCM equilibrium response predictions for a doubling of atmospheric CO2. The model considered is a version of the Oregon State University (OSU) atmospheric general circulation model (AGCM). The study consists of three stages. In the first stage we examine the spatial structure of changes in the annual mean and annual cycle for surface air temperature, mean sea-level pressure (SLP) and precipitation rate. Signal-to-noise (S/N) ratios or equivalent test statistics are then computed (using the 1×CO2 and 2×CO2 data) in order to identify variables most useful for detection purposes. Changes in both means and variances are considered as possible detection parameters. The highest S/N ratios are obtained for annual-mean and winter surface air temperature, and the lowest S/N ratios are obtained for SLP. There are significant increases in the temporal and spatial variability of precipitation, and significant decreases in the the temporal and spatial variability of surface air temperature.
A stochastic model of climate variability is considered in which slow changes of climate are explained as the integral response to continuous random excitation by short period “weather” disturbances. The coupled ocean-atmosphere-cryosphere-land system is divided into a rapidly varying “weather” system (essentially the atmosphere) and a slowly responding “climate” system (the ocean, cryosphere, land vegetation, etc.). In the usual Statistical Dynamical Model (SDM) only the average transport effects of the rapidly varying weather components are parameterised in the climate system. The resultant prognostic equations are deterministic, and climate variability can normally arise only through variable external conditions. The essential feature of stochastic climate models is that the non-averaged “weather” components are also retained. They appear formally as random forcing terms. The climate system, acting as an integrator of this short-period excitation, exhibits the same random-walk response characteristics as large particles interacting with an ensemble of much smaller particles in the analogous Brownian motion problem. The model predicts “red” variance spectra, in qualitative agreement with observations. The evolution of the climate probability distribution is described by a Fokker-Planck equation, in which the effect of the random weather excitation is represented by diffusion terms. Without stabilising feedback, the model predicts a continuous increase in climate variability, in analogy with the continuous, unbounded dispersion of particles in Brownian motion (or in a homogeneous turbulent fluid). Stabilising feedback yields a statistically stationary climate probability distribution. Feedback also results in a finite degree of climate predictability, but for a stationary climate the predictability is limited to maximal skill parameters of order 0.5.
A general circulation model for the ocean is used to investigate the
interaction between the global-scale thermohaline circulation and the
salinity distribution. It is shown that an equatorially asymmetric
circulation can be maintained even under equatorially symmetric basin
geometry and surface forcing. Multiple equilibrium solutions are
obtained for the same forcing by perturbing the high-latitude salinity
field in an otherwise equatorially symmetric initial condition. The
timescale of the transition from the symmetric circulation to an
asymmetric circulation depends critically on the sign of the initial
salinity perturbation.
When driven under "mixed boundary conditions", coarse resolution ocean
general circulation models (OGCMs) generally show a high sensitivity of
the present-day thermohaline circulation against perturbations. We will
show that an alternative formulation of the boundary condition for
temperature, a mixture of prescribed heat fluxes and additional
restoring of the sea surface temperature to a climatological boundary
temperature with a longer time constant, drastically alters the
stability of the modes of the thermohaline circulation. The results from
simulations with the Hamburg large-scale geostrophic OGCM indicate that
the stability of the mode of the thermohaline circulation with formation
of North Atlantic deepwater increases, if the damping of sea surface
temperature anomalies is reduced, whereas the opposite is true for the
mode without North Atlantic deep water formation. It turns out that the
formulation of the temperature boundary condition also affects the
variability of the model.
Multi-century simulations with a simplified coupled ocean-atmosphere model are described. These simulations reveal an impressive range of variability on decadal and longer time scales, in addition to the dominant interannual El Niño/Southern Oscillation signal that the model originally was designed to simulate. Based on a very large sample of century-long simulations, it is nonetheless possible to identify distinct model parameter sensitivities that are described here in terms of selected indices. Preliminary experiments motivated by general circulation model results for increasing greenhouse gases suggest a definite sensitivity to model global warming. While these results are not definitive, they strongly suggest that coupled air-sea dynamics figure prominantly in global change and must be included in models for reliable predictions.
A global ocean general circulation model is used to simulate the present-day ocean climate using four different latitude /longitude grid spacings. Horizontal resolution varies from 1/2° à 1/2° -the highest attained to date in an ocean circulation model with global coverage-to a coarse 4° à 4° grid traditionally used in climate modelling. This study addresses the question of whether resolution of smaller-scale circulations is necessary in order to simulate the large-scale ocean climate correctly. Results indicate that large-scale circulation is rather sluggish at 4° à 4° resolution but surprisingly insensitive to grid spacing for resolutions better than 2° à 2°, even considering the constraints imposed on the model by surface boundary conditions and by robust diagnostic'' forcing near the poles and below the thermocline. Simulated transport of heat from the warm tropics to cooler higher latitudes is not overly sensitive to grid spacing for resolutions better than 2° à 2°. Furthermore, heat transport does not increase at 1/2° à 1/2° resolution when sub-gridscale mixing of heat and momentum are altered so that mesoscale eddies appear in the model. These results support inferences from earlier studies (based on simplified, limited-domain circulation models) that mesoscale eddies make little net contribution to poleward heat transport by the oceans. They suggest that for global climate modeling, the substantial computer resources required to explicitly resolve ocean mesoscale eddies might be better spent on improving emulations of other components of the climate system. Making this conclusion definite, however, requires a global ocean simulation that is fully eddy-resolving and that relaxes the artificial constraints of the present simulation, which tend to force the results toward present ocean climatology.
The Department of Energy Workshop on the First Detection of Carbon Dioxide Effects was held in Harpers Ferry, West VA., on 8-10 June 1981 to develop a research strategy that would provide the basis for early identification of the expected COâ-induced response so that model projections of a warmer climate and of impacts on the biosphere can either be confirmed, rejected, or modified. A three-part strategy was developed, requiring: 1) determining whether a climatic or biospheric change has occurred, 2) considering the role of other factors that might contribute to or mask the COâ-induced response, and 3) isolating the climatic and biospheric response to increasing COâ concentrations. This third task is to be accomplished by the statistical analysis of a COâ-specific set of climatic and biospheric parameters that must respond together as theoretically predicted if COâ is to be judged as the most probable causal factor. Recommendations by authors of submitted papers and by panel members are listed.
A series of numerical experiments involving long‐time integrations are conducted using the Bryan‐Cox Ocean General Circulation Model under mixed surface boundary conditions (i.e. a Newtonian restoring surface boundary condition on temperature and a specified flux boundary condition on salinity). Under steady forcing the system oscillates with significant energy at decadal period. This oscillation is shown to be an advective phenomenon, associated with the propagation of salinity and temperature anomalies from the region between the subtropical and subpolar gyres, where they are generated, to the eastern boundary, where deep water is formed. Furthermore, the oscillation is characterized by the fluctuation of the thermohaline circulation between a state in which deep water is formed and a collapsed state in which no deep water is formed. Over the period of the oscillation the poleward heat transport changes by as much as a factor of 3 at certain latitudes. The anomalies are initially formed by the upwelling of warm, saline waters that are being transported polewards by a western boundary current that has separated from the coast. The observed decadal variability is robust in that it is present in all numerical experiments (12 and 33 vertical‐level models; one and two hemisphere models; synchronous and asynchronous integrations). Crucial to the existence of the variability is the use of a low vertical eddy viscosity coefficient.
Explosions of 300 lbs of TNT at 1 km depth off Perth, Australia were recorded on Bermuda hydrophones, demonstrating 30 years age the feasibility of global acoustic transmissions. Climate-induced changes in ocean temperature (and hence in sound speed) can be monitored by measuring travel time changes of acoustic signals from remote powerful sources. Warming induced now at the sound axis by CO2 and other greenhouse gases is estimated at 0.005°C per year, too small to be measured locally in the presence of 1°C rms noise from gyre scale and mesoscale fluctuations. The associated rate of decrease in travel time from greenhouse warming (a global measure of temperature rise) is estimated at 0.1 to 0.2 s per year. This climatic signal should be detectable above the gyre and mesoscale noise (less than 1 s rms), given a program of measurements carried out over a decade. An acoustic source at Heard Island in the south Indian Ocean has direct oceanic paths into all five ocean basins—westward to South Georgia, Brazil, South Africa and Bermuda; eastward to Tasmania, New Zealand, Tahiti, Hawaii, San Francisco and Oregon; northward to Indonesia and; southward to Antarctica. A feasibility experiment is planned.
A quantitative search for a theoretically-predicted CO2 signal in surface air temperature data extending back to 1899 was marginally successful in a statistical sense. However, the nature of the signal-strength time series suggested this result in an artifact of large-scale decadal variations at the beginning and end of the record. Application of the ''fingerprint'' strategy to three different global fields of climate variables over the last 25 to 35 years showed no significant CO2 signal. The analysis pointed up the need to use (1) model S/N ratios in selecting fields for subsequent analysis and (2) multiple fields in the detection process. Most important, we found the primary pattern of natural air temperature variability to be very similar to the expected CO2 signal, thus suggesting the air temperature field is not the best place to attempt early detection of the CO2 signal. By contrast, the primary pattern of natural variability and expected CO2 signals in the ocean's surface temperature is substantially different suggesting the oceans as a low-noise environment in which to attempt early detection.
Several approaches to detecting the existence of a theoretically predicted COâ-induced signal in the global temperature field are investigated. It appears that a relatively thin network of observing stations can, when properly analyzed, provide a first-order estimate of global-scale temperature change and that this measurement is not necessarily the global average temperature. Using these stations, it is possible to estimate the strength of an a priori COâ signal in the three dimensional tropospheric temperature field. This signal is derived from two different general circulation model simulations, both with some form of interactive oceans. The observed signal strength over 1960-1980 is roughly 0.5-1.0 times that expected theoretically. The observed signal demonstrated a trend that is marginally significant.
The first assessments of the potential climatic effects of increased COâ were performed using simplified climate models, namely, energy balance models (EBMs) and radiative-convective models (RCMs). The feedback processes in RCMs include water vapor feedback, moist adiabatic lapse rate feedback, cloud altitude feedback, cloud cover feedback, cloud optical depth feedback, and surface albedo feedback. However, these feedbacks can be predicted credibly only by physically based models that include the essential dynamics and thermodynamics of the feedback processes. Such physically based models are the general circulation models (GCMs). The earliest GCM simulations of COâ-induced climate change were performed without the annual insolation cycle. The first GCM simulation of the seasonal variation of COâ-induced climate change was performed for a COâ quadrupling and obtained annual global mean surface temperature and precipitation changes of 4.1°C and 6.7%, respectively. Recently, three COâ-doubling experiments have been performed with GCMs that include the annual insolation cycle. These seasonal simulations give an annual global mean warming of 3.5° to 4.2°C and precipitation increases of 7.1 to 11%. The geographical distributions of the COâ-induced warming obtained by the recent simulations agree qualitatively but not quantitatively. Furthermore, the precipitation and soil moisture changes do not agree quantitatively and even show qualitative differences. In order to improve the state of the art in simulating the equilibrium climatic change induced by increased COâ concentrations, it is recommended first that the contemporary GCM simulations be analyzed to determine the feedback processes responsible for their differences and second that the parameterization of these processes in the GCMs be validated against highly detailed models and observations.
The sensitivity of the global ocean circulation to changes in surface heat flux forcing is studied using the Hamburg Large Scale Geostrophic (LSG) ocean circulation model. The simulated mean ocean circulation for appropriately chosen surface forcing fields reproduces the principal water mass properties, residence times, and large-scale transport properties of the observed ocean circulation quite realistically within the constraints of the model resolution. However, rather minor changes in the formulation of the high-latitude air-sea heat flux can produce dramatic changes in the structure of the ocean circulation. These strongly affect the deep-ocean overturning rates and residence times, the oceanic heat transport, and the rate of oceanic uptake of CO2. The sensitivity is largely controlled by the mechanism of deep-water formation in high latitudes. -Authors
Although it is widely believed that increasing atmospheric CO2 levels will cause noticeable global warming, the effects are not yet detectable, possibly because of the 'noise' of natural climatic variability. An examination of the spatial and seasonal distribution of signal-to-noise ratio shows that the highest values occur in summer and annual mean surface temperatures averaged over the Northern Hemisphere or over mid-latitudes. The spatial and seasonal characteristics of the early twentieth century warming were similar to those expected from increasing CO2 based on an equilibrium response model. This similarity may hinder the early detection of CO2 effects on climate.
A simple slab ocean of 50 m depth, which allows for seasonal ocean heat storage but no ocean heat transport, is coupled to a global spectral general circulation model with global domain, realistic geography, and computed clouds. The paper first describes the atmospheric and oceanic aspects of the model. Following that there is a general discussion of the model control experiment and comparison with observed data. The next two sections describe the zonal mean and geographical responses to a doubling of CO//2 concentration. Following those there is a section with a discussion of the swamp model results compared to the present mixed-layer model results. Finally, the last section draws conclusions from the experiments.
Tree-ring data have been used to reconstruct the mean summer (April-August) temperature of northern Fennoscandia for each year from AD 500 to the present. Summer temperatures have fluctuated markedly on annual, decadal and century timescales. There is little evidence for the existence of a Medieval Warm Epoch, and the Little Ice Age seems to be confined to the relatively short period between 1570 and 1650. This challenges the popular idea that these events were the major climate excursions of the first millennium, occurring synchronously throughout Europe in all seasons. An analysis of past warming trends suggests that any summer warming induced by greenhouse gases may not be detectable in this region until after 2030.
The effect of changes in atmospheric carbon dioxide concentrations and sulphate aerosols on near-surface temperature is investigated using a version of the Hadley Centre atmospheric model coupled to a mixed layer ocean. The scattering of sunlight by sulphate aerosols is represented by appropriately enhancing the surface albedo. On doubling atmospheric carbon dioxide concentrations, the global mean temperature increases by 5.2 K. An integration with a 39% increase in CO{sub 2}, giving the estimated change in radiative heating due to increases in greenhouse gases since 1900, produced an equilibrium warming of 2.3 K, which, even allowing for oceanic inertia, is significantly higher than the observed warming over the same period. Furthermore, the simulation suggests a substantial warming everywhere, whereas the observations indicate isolated regions of cooling, including parts of the northern midlatitude continents. The addition of an estimate of the effect of scattering by current industrial aerosols (uncertain by a factor of at least 3) leads to improved agreement with the observed pattern of changes over the northern continents and reduces the global mean warming by about 30%. Doubling the aerosol forcing produces patterns that are still compatible with the observations, but further increase leads to unrealistically extensive cooling in the midlatitudes. The diurnal range of surface temperature decreases over most of the northern extratropics on increasing CO{sub 2}, in agreement with recent observations. The addition of the current industrial aerosol had little detectable effect on the diurnal range in the model because the direct effect of reduced solar heating at the surface is approximately balanced by the indirect effects of cooling. Thus, the ratio of the reduction in diurnal range to the mean warming is increased, in closer agreement with observations. Results from further sensitivity experiments with larger increases in aerosol and CO{sub 2} are presented.
For more than a decade, numerical models have predicted that increasing CO/sub 2/ concentrations would cause a warming of the global climate, with amplified warming in polar regions and cooling in the stratosphere. Such changes, however, are not now obvious in the climatic record, although it is possible that the changes are being obscured by natural climatic fluctuations or by the perturbing influence of other factors. Indeed, a number of observational studies have found, after attempting to account for the possible climatic changes caused by such other factors as volcanoes and changes in solar activity, that the predicted global warming may be occurring. Studies of the polar regions and stratosphere, however, do not yet show the expected changes. The Department of Energy Workshop on First Detection of CO/sub 2/ Effects was held to develop a research strategy that would provide the basis for early identification of the expected CO/sub 2/-induced response so that model projections of a warmer climate and of impacts on the biosphere can either be confirmed, rejected, or modified. A three-part strategy was developed requiring (1) determination of whether a climate or biospheric change has occurred, (2) consideration of the role of other factors that might contribute to or mask the CO/sub 2/-induced response, and (3) isolation of the climatic and biospheric response to increasing CO/sub 2/ concentrations. This third task is to be accomplished by statistical analysis of a CO/sub 2/-specific set of climatic and biospheric parameters that must respond together as theoretically predicted if CO/sub 2/ is to be judged as the most prabable causal factor.
An optimal linear filter (fingerprint) is derived for the detection of a given time-dependent, multivariate climate change signal in the presence of natural climate variability noise. Application of the fingerprint to the observed (or model simulated) climate data yields a climate change detection variable with maximal signal-to-noise ratio. The optimal fingerprint is given by the product of the assumed signal pattern and the inverse of the climate variability covariance matrix. The data can consist of any climate dataset for which estimates of the natural variability covariance matrix exist. The single-pattern analysis is readily generalized to the multipattern case of a climate change signal lying in a prescribed signal pattern space. Multipattern detection methods can be applied either to test the statistical significance of individual components of a predicted multicomponent climate change response, using separate single-pattern detection tests, or to determine the statistical significance of the complete signal, using a multivariate test. The difference in direction of the assumed signal pattern and computed optimal fingerprint vector allows alternative interpretations of the estimated signal associated with the set of optimal detectors. The present analysis yields an estimated signal lying in the assumed signal space, whereas an earlier analysis of the time-independent detection problem by Hasselman yielded an estimated signal in the computed fingerprint space. The different interpretations can be explained by different choices of the metric used to relate the signal space to the fingerprint space (inverse covariance matrix versus standard Euclidean metric, respectively). Two simple natural variability models are considered: a space-time separability model, and an expansion in terms of POPs (principal oscillation patterns). For each model the application of the optimal fingerprint method is illustrated by an example. 45 refs., 5 figs., 2 tabs.
Book review of the intergovernmental panel on climate change report on global warming and the greenhouse effect. Covers the scientific basis for knowledge of the future climate. Presents chemistry of greenhouse gases and mathematical modelling of the climate system. The book is primarily for government policy makers.
Development is described of a Comprehensive Ocean-Atmosphere Data Set
(COADS)-the result of a cooperative project to collect global weather
observations taken near the ocean's surface since 1854, primarily from
merchant ships, into a compact and easily used data set. As background,
a historical overview is given of how archiving of these marine data has
evolved from 1854, when systematic recording of shipboard meteorological
and oceanographic observations was first established as an international
activity. Input data sets used for COADS are described, as well as the
processing steps used to pack input data into compact binary formats and
to apply quality controls for identification of suspect weather elements
and duplicate marine reports. Seventy-million unique marine reports for
1854-1979 were output from initial processing. Further processing is
described, which created statistical summaries for each month of each
year of the period, using 2° latitude × 2° longitude
boxes. Monthly summary products are available giving 14 statistics (such
as the median and the mean) for each of eight observed variables (air
and sea-surface temperatures, scalar and vector wind, pressure,
humidity, and cloudiness), plus 11 derived variables. Examples of known
temporal, spatial, and methodological inhomogeneities in marine data,
and plans for periodic updates to COADS, including an update through
1986 scheduled for completion by early 1988, are presented.
Climatic fluctuations on a decadal timescale in the North Atlantic in a global ocean general circulation model were considered. The analysis was carried out for the 3800-year stochastic forcing simulation of Mikolajewicz and Maier-Reimer in which the Hamburg Large-Scale Geostrophic ocean model was driven by monthly climatologies of wind stress, air temperature, and freshwater flux with superimposed white noise freshwater fluxes with an amplitude of about 16 mm/month. We applied a Principal Oscillation Pattern analysis to the vector time series of the upper level salinity fields, so that the examined fluctuations appear as estimated eigenmodes of the system. In addition to an oscillation with a period of 320 years as already described by Mikolajewicz and Maier-Reimer, we found a broadband Principal Oscillation Pattern with a timescale of the order of 10 to 40 years. It describes the generation of salinity anomalies in the Labrador Sea and the following discharge into the North Atlantic. In sensitivity experiments we clarified that the source of the variability lies in the Labrador Sea and showed that the generation of the salinity anomalies is mainly due to an undisturbed local integration of the white noise freshwater fluxes
GLOBAL mean temperatures show considerable variability on all timescales. The causes of this variability are usually classified as external or internal1, and the variations themselves may be usefully subdivided into low-frequency variability (timescale ≳= 10 years) and high-frequency variability (≲=10 years). Virtually nothing is known about the nature or magnitude of internally generated, low-frequency variability. There is some evidence from models, however, that this variability may be quite large1,2, possibly causing fluctuations in global mean temperature of up to 0.4 °C over periods of thirty years or more (see ref. 2, Fig. 1). Here we show how the ocean may produce low-frequency climate variability by passive modulation of natural forcing, to produce substantial trends in global mean temperature on the century timescale. Simulations with a simple climate model are used to determine the main controls on internally generated low-frequency variability, and show that natural trends of up to 0.3 °C may occur over intervals of up to 100 years. Although the magnitude of such trends is unexpectedly large, it is insufficient to explain the observed global warming during the twentieth century.
RECENTLY, Kiehl and Briegleb1 evaluated the radiative forcing associated with the capacity of atmospheric sulphate aerosols to reflect solar radiation back into space, and compared this with the forcing associated with atmospheric greenhouse gases. They found that the (negative) climate forcing by the aerosols has strong regional character, with the greatest forcing over Northern Hemisphere land surfaces, whereas the (positive) forcing by greenhouse gases is distributed almost equally between the hemispheres and varies mainly as a function of latitude. Here we present simulations of the response of the climate system to these two types of forcing. We find that the global response to aerosol forcing is regionally heterogeneous, with a distribution that is different from the forcing pattern. The simulations also imply that, for equal magnitudes of forcing, the temperature response is markedly greater for carbon dioxide than for aerosol forcing. We conclude that to predict the global mean climate response to global mean forcing, it is necessary to separate out the different components of the forcing to which the climate system is sensitive.
The use of an 'effective' CO2 concentration to simulate the combined greenhouse effect of CO2 and the trace gases CH4, N2O, CFC-11 and CFC-12 is open to question, because the radiative-forcing behaviour of CO2 is very different from that of these other gases. Model simulations show that different radiative forcing can lead to quite different climatic effects. The thermal infrared opacity of these trace gases therefore needs to be explicitly accounted for when attempting to predict the climate response to increasing concentrations of greenhouse gases.
Changes in surface air temperature resulting from a doubling in atmospheric carbon dioxide drive changes in ocean circulation. Results from an ocean general circulation model project a global mean sea level rise from thermal expansion alone to be 19cm in 50 years. Regional values, however, can vary: a rise of 40cm is projected in the North Atlantic (owing to reduction of deep-water formation), whereas the level of the Ross Sea actually falls through changes in ocean circulation.
Oxygen isotope analysis of anew ice core from the crest of the Greenland ice sheet reveals a climatic record of the past 1,420 years. Climatic changes of medium frequencies are in phase with corresponding changes in Iceland and England, whilst long-term changes at mid Atlantic longitudes are out of phase with Europe and North America. Reconciliation with Norse history suggests a strong climatic impact, and a parallel is drawn to the present critical situation of the human society.
SINCE the late nineteenth century, the global mean surface air temperature has been increasing at the rate of about 0.5 °C per century1–3, but our poor understanding of low-frequency natural climate variability has made it very difficult to determine whether the observed warming trend is attributable to the enhanced green-house effect associated with increased atmospheric concentrations of greenhouse gases4,5. Here we evaluate the observed warming trend using a 1,000-year time series of global temperature obtained from a mathematical model of the coupled ocean–atmosphere–land system. We find that the model approximately reproduces the magnitude of the annual to interdecadal variation in global mean surface air temperature. But throughout the simulated time series no temperature change as large as 0.5 °C per century is sustained for more than a few decades. Assuming that the model is realistic, these results suggest that the observed trend is not a natural feature of the interaction between the atmosphere and oceans. Instead, it may have been induced by a sustained change in the thermal forcing, such as that resulting from changes in atmospheric greenhouse gas concentrations and aerosol loading.
Tree-ring data have been used to reconstruct the mean summer (April-August) temperature of northern Fennoscandia for each year from AD 500 to the present. Summer temperatures have fluctuated markedly on annual, decadal and century timescales. There is little evidence for the existence of a Medieval Warm Epoch, and the Little Ice Age seems to be confined to the relatively short period between 1570 and 1650. This challenges the popular idea that these events were the major climate excursions of the first millennium, occurring synchronously throughout Europe in all seasons. An analysis of past warming trends suggests that any summer warming induced by greenhouse gases may not be detectable in this region until after 2030.
IT has been hypothesized that climate may be noticeably affected by changes in cloud condensation nuclei (CCN) concentrations, caused either by changes in the flux of dimethylsulphide (DMS) from the oceans1,2 and/or by man-made increases in the flux of sulphur dioxide (SO2) into the atmosphere3. When oxidized, the sulphur compounds produce non-sea-salt sulphate (n.s.s.-SO2−
4,) aerosols, which may act as CCNs. The CCN changes affect climate by altering the number density and size distribution of droplets in clouds, and hence their albedo. Here I am concerned primarily with the possible effects of SO2. Because the increase in SO2 emissions has been largely in the Northern Hemisphere, this raises the possibility of a cooling of the Northern Hemisphere relative to the Southern3. By comparing observed differences in hemispheric-mean temperatures with results from a simple climate model, one can place limits on the possible magnitude of any SO2-derived forcing. The upper limit is sufficiently large that the effects of SO2 may have significantly offset the temperature changes that have resulted from the greenhouse effect.
The relationship between greenhouse-gas forcing, global mean temperature change and sea-level rise due to thermal expansion of the oceans is investigated using upwelling–diffusion and pure diffusion models. The sensitivities of sea-level to short-timescale forcing and deep-water formation rate changes are examined. The greenhouse-gas-induced thermal expansion contribution to sea-level rise between 1880 and 1985 is estimated at 2–5 cm. Projections are made to the year 2025 for different forcing scenarios. For the period 1985–2025 the estimate of greenhouse-gas-induced warming is 0.6–1.0 °C. The concomitant oceanic thermal expansion would raise sea level by 4–8 cm.