Detection Of Anthropogenic Climate Change Research Articles

Early detection of anthropogenic climate change signals in the ocean interior

Robust detection of anthropogenic climate change is crucial to: (i) improve our understanding of Earth system responses to external forcing, (ii) reduce uncertainty in future climate projections, and (iii) develop efficient mitigation and adaptation plans. Here, we use Earth system model projections to establish the detection timescales of anthropogenic signals in the global ocean through analyzing temperature, salinity, oxygen, and pH evolution from surface to 2000 m depths. For most variables, anthropogenic changes emerge earlier in the interior ocean than at the surface, due to the lower background variability at depth. Acidification is detectable earliest, followed by warming and oxygen changes in the subsurface tropical Atlantic. Temperature and salinity changes in the subsurface tropical and subtropical North Atlantic are shown to be early indicators for a slowdown of the Atlantic Meridional Overturning Circulation. Even under mitigated scenarios, inner ocean anthropogenic signals are projected to emerge within the next few decades. This is because they originate from existing surface changes that are now propagating into the interior. In addition to the tropical Atlantic, our study calls for establishment of long-term interior monitoring systems in the Southern Ocean and North Atlantic in order to elucidate how spatially heterogeneous anthropogenic signals propagate into the interior and impact marine ecosystems and biogeochemistry.

Intrinsic Century‐Scale Variability in Tropical Pacific Sea Surface Temperatures and Their Influence on Western US Hydroclimate

AbstractHydroclimate variability of the southwest United States (SWUS) is influenced by the tropical Pacific Ocean, particularly through the teleconnection to El Niño/Southern Oscillation (ENSO), which is expected to be altered by climate change. Natural variability in this teleconnection has not been robustly quantified, complicating the detection of anthropogenic climate change. Here, we use a linear inverse model (LIM) to quantify natural variability in the ENSO‐SWUS teleconnection. The LIM yields realistic teleconnection patterns with century‐scale variability comparable to simulations from the Last Millennium Ensemble project and the Climate Model Intercomparison Project Phases 5 and 6. The variability quantified by the LIM illuminates two aspects of our understanding of ENSO and its impacts: the inherent statistics of the observable system can produce century‐long periods with a wide range of correlations to SWUS hydroclimate, including nonsignificant correlations, and thus that detecting changes in ENSO‐related hydroclimate variability is challenging in a changing climate.

Improving Climate Change Detection through Optimal Seasonal Averaging: The Case of the North Atlantic Jet and European Precipitation

Abstract The detection of anthropogenic climate change can be improved by recognizing the seasonality in the climate change response. This is demonstrated for the North Atlantic jet [zonal wind at 850 hPa (U850)] and European precipitation responses projected by the climate models from phase 5 of CMIP (CMIP5). The U850 future response is characterized by a marked seasonality: an eastward extension of the North Atlantic jet into Europe in November–April and a poleward shift in May–October. Under the RCP8.5 scenario, the multimodel mean response in U850 in these two extended seasonal means emerges by 2035–40 for the lower-latitude features and by 2050–70 for the higher-latitude features, relative to the 1960–90 climate. This is 5–15 years earlier than when evaluated in the traditional meteorological seasons (December–February and June–August), and it results from an increase in the signal-to-noise ratio associated with the spatial coherence of the response within the extended seasons. The annual mean response lacks important information on the seasonality of the response without improving the signal-to-noise ratio. The same two extended seasons are demonstrated to capture the seasonality of the European precipitation response to climate change and to anticipate its emergence by 10–20 years. Furthermore, some of the regional responses (such as the Mediterranean precipitation decline and the U850 response in North Africa in the extended winter) are projected to emerge by 2020–25, according to the models with a strong response. Therefore, observations might soon be useful to test aspects of the atmospheric circulation response predicted by some of the CMIP5 models.

Moving up early detection

Natural climate variability complicates the detection of anthropogenic climate change in the twenty-first century. Now, research shows that evidence of human influence first emerges from sea level rather than temperature rise.

Detecting changes in seasonal precipitation extremes using regional climate model projections: Implications for managing fluvial flood risk

There is growing evidence of coherent, global patterns of change in annual precipitation and runoff with high latitudes experiencing increases consistent with climate model projections. This paper describes a methodology for estimating detection times for changes in seasonal precipitation extremes. The approach is illustrated using changes in UK precipitation projected by the European Union PRUDENCE climate model ensemble. We show that because of high variability from year to year and confounding factors, detection of anthropogenic climate change at regional scales is not generally expected for decades to come. Overall, the earliest detection times were found for 10 day winter precipitation totals with 10 year return period in SW England. In this case, formal detection could be possible within a decade from now if the climate model projections are realized. The outlook for changes in summer flash flood risk is highly uncertain. Our analysis further demonstrates that existing precautionary allowances for climate change used for flood management may not be sufficiently robust in NE England and east Scotland. These findings imply that for certain types of flood mechanism, adaptation decisions might have to be taken in advance of formally detected changes in flood risk. This reinforces the case for long‐term environmental monitoring and reporting of climate change indices at “sentinel” locations.

Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity

Abstract. Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years). Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

Detection‐attribution of global warming at the regional scale: How to deal with precipitation variability?

Given the strong natural variability of precipitation, detection of anthropogenic climate change has been mainly based on surface thermal indicators. It is here argued that precipitation variability, whatever its natural or anthropogenic origin, is likely to affect surface temperature and, thereby, to hamper the detection‐attribution of climate change at the regional scale. This issue is illustrated over Sudan and Sahel using the outputs of eleven coupled general circulation models having participated in the latest IPCC simulations. Like in situ observations, models show a strong relationship between detrended seasonal anomalies of surface air temperature and precipitation during the summer monsoon season. Linear regressions are then used to remove the precipitation influence on observed and simulated linear trends in surface air temperature. The results indicate that this strategy is efficient to reduce the spread in the simulated surface warming and make it more consistent with the instrumental record over recent decades.

Nonlinear statistical attribution and detection of anthropogenic climate change using a simulated annealing algorithm

¶The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/ underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986). In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Nino/Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference. It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CM-FSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.

Evaluation of Northern Hemisphere natural climate variability in multiple temperature reconstructions and global climate model simulations

The detection of anthropogenic climate change in observations and the validation of climate models both rely on understanding natural climate variability. To evaluate internal climate variability, we apply spectral analysis to time series of surface air temperature (SAT) from nine coupled general circulation model (GCM) simulations, three recent global paleotemperature reconstructions, and Northern Hemisphere (NH) instrumental records. Our comparison is focused on the NH due to the greater spatial and temporal coverage and validation of the available NH temperature reconstructions. The paleotemperature reconstructions capture the general magnitude of NH climate variability, but not the precise variance and specific spatial, temporal, or periodic signals demonstrated in the instrumental record. The models achieved varying degrees of success for each measure of variability analyzed, with none of the models consistently capturing the appropriate variability. In general, the models performed best in the analysis of combined mean annual land and marine variability.

Attribution and detection of anthropogenic climate change using a backpropagation neural network

The climate system can be regarded as a dynamic nonlinear system. Thus traditional linear statistical methods are not suited to describe the nonlinearities of this system which renders it necessary to find alternative statistical techniques to model those nonlinear properties. In addition to an earlier paper on this subject (WALTER et al., 1998), the problem of attribution and detection of the observed climate change is addressed here using a nonlinear Backpropagation Neural Network (BPN). In addition to potential anthropogenic influences on climate (CO 2 -equivalent concentrations, called greenhouse gases, GHG and SO 2 emissions) natural influences on surface air temperature (variations of solar activity, volcanism and the El Nino/Southern Oscillation phenomenon) are integrated into the simulations as well. It is shown that the adaptive BPN algorithm captures the dynamics of the climate system, i.e. global and area weighted mean temperature anomalies, to a great extent. However, free parameters of this network architecture have to be optimized in a time consuming trial-and-error process. The simulation quality obtained by the BPN exceeds the results of those from a linear model by far; the simulation quality on the global scale amounts to 84% explained variance. Additionally the results of the nonlinear algorithm are plausible in a physical sense, i.e. amplitude and time structure. Nevertheless they cover a broad range, e.g. the GHG-signal on the global scale ranges from 0.37 K to 1.65 K warming for the time period 1856-1998. However the simulated amplitudes are situated within the discussed range (HOUGHTON et al., 2001). Additionally the combined anthropogenic effect corresponds to the observed increase in temperature for the examined time period. In addition to that, the BPN succeeds with the detection of anthropogenic induced climate change on a high significance level. Therefore the concept of neural networks can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.

Where to Look for Anthropogenic Climate Change in the Ocean

A coupled climate model has been used to provide preliminary guidance on which ocean observations will be most useful for early detection of anthropogenic climate change. Given the sparsity of historical ocean measurements, early detection may need to be based on the differences between two snapshots of the ocean. This is the basis for this analysis. Sea surface temperature is shown to give a particularly good signal-to-noise ratio, justifying the use of surface temperature in almost all detection studies to date. Meridional heat transports are unlikely to be useful for detection of anthropogenic change, while measurements of the thermohaline circulation may require continuous time series. Previously subsurface temperature has been used in a integrated way to detect change. It is shown that subsurface temperature and salinity have the potential for detecting anthropogenic change on both pressure and density surfaces over 30 years. These results highlight the potential high signal-to-noise ratio in particular areas of the ocean: the Arctic and Atlantic, the North Pacific, and the Southern Ocean. Given that there already are historical measurements of the subsurface ocean temperature and salinity, this suggests that, in terms of detection of change in the ocean, monitoring of these should probably be one of the highest priorities and there should be an attempt to monitor regions (such as the Southern Ocean) that are historically data sparse. The results of this work will be of interest to observationalists and policy makers looking for an objective assessment of where to measure the ocean.

Detection of anthropogenic climate change using an atmospheric GCM

Atmosphere-only general circulation models are shown to be a useful tool for detecting an anthropogenic effect on climate and understanding recent climate change. Ensembles of atmospheric runs are all forced with the same observed changes in sea surface temperatures and sea-ice extents but differ in terms of the combinations of anthropogenic effects included. Therefore, our approach aims to detect the `immediate' anthropogenic impact on the atmosphere as opposed to that which has arisen via oceanic feedbacks. We have adapted two well-used detection techniques, pattern correlations and fingerprints, and both show that near-decadal changes in the patterns of zonal mean upper air temperature are well simulated, and that it is highly unlikely that the observed changes could be accounted for by sea surface temperature variations and internal variability alone. Furthermore, we show that for zonally averaged upper air temperature, internal `noise' in the atmospheric model is small enough that a signal emerges from the data even on interannual time scales; this would not be possible in a coupled ocean-atmosphere general circulation model. Finally, although anthropogenic forcings have had a significant impact on global mean land surface temperature, we find that their influence on the pattern of local deviations about this mean is so far undetectable. In order to achieve this in the future, as the signal grows, it will also be important that the response of the Northern Hemisphere mid-latitude westerly flow to changing sea surface temperatures is well simulated in climate model detection studies.

Detection of anthropogenic climate change in the world's oceans.

Large-scale increases in the heat content of the world's oceans have been observed to occur over the last 45 years. The horizontal and temporal character of these changes has been closely replicated by the state-of-the-art Parallel Climate Model (PCM) forced by observed and estimated anthropogenic gases. Application of optimal detection methodology shows that the model-produced signals are indistinguishable from the observations at the 0.05 confidence level. Further, the chances of either the anthropogenic or observed signals being produced by the PCM as a result of natural, internal forcing alone are less than 5%. This suggests that the observed ocean heat-content changes are consistent with those expected from anthropogenic forcing, which broadens the basis for claims that an anthropogenic signal has been detected in the global climate system. Additionally, the requirement that modeled ocean heat uptakes match observations puts a strong, new constraint on anthropogenically forced climate models. It is unknown if the current generation of climate models, other than the PCM, meet this constraint.

Effect of Observational Sampling Error on the Detection of Anthropogenic Climate Change*

Abstract The effect of sampling error in surface air temperature observations is assessed for detection and attribution of an anthropogenic signal. This error arises because grid-box values are based on varying densities of station and marine data. An estimate of sampling error is included in the application of an optimal detection and attribution method based on June–August trends over 50 yr. The detection and attribution method is applied using both the full spatial pattern of observed trends and spatial patterns from which the global mean warming has been subtracted. Including the effect of sampling error is found to increase the uncertainty in estimates of the greenhouse gas–plus–sulfate aerosol signal from observations by less than 2%–6% for recent trend patterns (1949–98), and 3%–8% for signal estimates from observations in the first half of the twentieth century. Random instrumental error shows even smaller effects. However, the effects of systematic instrumental errors, such as changes in measurem...

Implications of changes in the northern hemisphere circulation for the detection of anthropogenic climate change

The first principal component of Northern Hemisphere sea level pressure, known as the Arctic Oscillation (AO) index, has increased significantly in recent winters, and this change is associated with ∼30% of Northern Hemisphere January–March warming. We examine the AO in a model used to detect anthropogenic influence on climate, and find that it exhibits no systematic trend in response to greenhouse gas, sulphate aerosol, or ozone forcing. To test the significance of this discrepancy for anthropogenic climate change detection, we include the spatio‐temporal pattern of temperature change associated with the observed AO in the set of forcing‐response “fingerprints” used to account for observed changes, thus separating temperature change associated with the AO from a residual. We find that the detection of a global response to both anthropogenic greenhouse gases and sulphate aerosols is robust to this exclusion of AO‐related warming.