Detection and attribution of seasonal temperature changes in India with climate models in the CMIP5 archive

2015 ◽  
Vol 7 (1) ◽  
pp. 83-102 ◽  
Author(s):  
P. Sonali ◽  
D. Nagesh Kumar
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Causative Factor ◽  
Temporal Variations ◽  
Seasonal Temperature ◽  
Temperature Changes ◽  
Model Simulations ◽  
Internal Climate Variability ◽  
Detection And Attribution ◽  
Attribution Analysis

This study analyzes the change in annual and seasonal maximum and minimum temperature (Tmax and Tmin) during the period 1950–2005 (i.e., second half of the 20th century). In-depth analyses have been carried out for all over India as well as for five temperature homogenous regions of India separately. First, the temporal variations of annual and seasonal Tmax and Tmin are analyzed, employing the trend free pre-whitening Mann-Kendall approach. Secondly, it is assessed whether the observations contain significant signals above the natural internal variability determined from a long ‘piControl’ experiment, using Monte Carlo simulation. Thirdly, fingerprint based formal detection and attribution analysis is used to determine the signal strengths of observed and model simulations with respect to different considered experiments. Finally, these signal strengths are compared to attribute the observed changes in Tmax and Tmin to different factors. All the model simulated datasets are retrieved from the CMIP5 archive. It is noticed that the emergence of observed trends is more pronounced in Tmin compared to Tmax. Although observed changes are not solely associated with one specific causative factor, most of the changes in Tmin lie above the bounds of natural internal climate variability.

scholarly journals Robust detection of forced warming in the presence of potentially large climate variability

2021 ◽  
Author(s):  
Sebastian Sippel ◽  
Nicolai Meinshausen ◽  
Eniko Székely ◽  
Erich Fischer ◽  
Angeline G. Pendergrass ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Models ◽  
Climate Model ◽  
Decadal Variability ◽  
Internal Variability ◽  
Warming Trend ◽  
Robust Detection ◽  
Internal Climate Variability ◽  
Detection And Attribution

<p>Warming of the climate system is unequivocal and substantially exceeds unforced internal climate variability. Detection and attribution (D&A) employs spatio-temporal fingerprints of the externally forced climate response to assess the magnitude of a climate signal, such as the multi-decadal global temperature trend, while internal variability is often estimated from unforced (“control”) segments of climate model simulations (e.g. Santer et al. 2019). Estimates of the exact magnitude of decadal-scale internal variability, however, remain uncertain and are limited by relatively short observed records, their entanglement with the forced response, and considerable spread of simulated variability across climate models. Hence, a limitation of D&A is that robustness and confidence levels depend on the ability of climate models to correctly simulate internal variability (Bindoff et al., 2013).</p><p>For example, the large spread in simulated internal variability across climate models implies that the observed 40-year global mean temperature trend of about 0.76°C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5σ), corresponding to vanishingly small probabilities if taken at face value. But the observed trend would exceed the standard deviation of a few `high-variability' climate models `only' by a factor of about two, thus unlikely to be internally generated but not practically impossible given unavoidable climate system and observational uncertainties. This illustrates the key role of model uncertainty in the simulation of internal variability for D&A confidence estimates.</p><p>Here we use a novel statistical learning method to extract a fingerprint of climate change that is robust towards model differences and internal variability, even of large amplitude. We demonstrate that externally forced warming is distinct from internal variability and detectable with high confidence on any state-of-the-art climate model, even those that simulate the largest magnitude of unforced multi-decadal variability. Based on the median of all models, it is extremely likely that more than 85% of the observed warming trend over the last 40 years is externally driven. Detection remains robust even if their main modes of decadal variability would be scaled by a factor of two. It is extremely likely that at least 55% of the observed warming trend over the last 40 years cannot be explained by internal variability irrespective of which climate model’s natural variability estimates are used.</p><p>Our analysis helps to address this limitation in attributing warming to external forcing and provides a novel perspective for quantifying the magnitude of forced climate change even under uncertain but potentially large multi-decadal internal climate variability. This opens new opportunities to make D&A fingerprints robust in the presence of poorly quantified yet important features inextricably linked to model structural uncertainty, and the methodology may contribute to more robust detection and attribution of climate change to its various drivers.</p><p> </p><p>Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.</p><p>Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science. <em>Nat Clim Change</em> <strong>9</strong>(3), pp. 180-182.</p>

The North Atlantic Ocean as a Modulator of Vegetation Greening/Browning in the Northern High Latitudes?

2021 ◽  
Author(s):  
Leonard F. Borchert ◽  
Alexander J. Winkler
Keyword(s):  
Climate Variability ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Internal Variability ◽  
Dominant Mode ◽  
High Latitudes ◽  
Model Simulations ◽  
Internal Climate Variability ◽  
The North ◽  
The North Atlantic

<p>Vegetation in the northern high latitudes shows a characteristic pattern of persistent changes as documented by multi-decadal satellite observations. The prevailing explanation that these mainly increasing trends (greening) are a consequence of external CO<sub>2</sub> forcing, i.e., due to the ubiquitous effect of CO2-induced fertilization and/or warming of temperature-limited ecosystems, however does not explain why some areas also show decreasing trends of vegetation cover (browning). We propose here to consider the dominant mode of multi-decadal internal climate variability in the north Atlantic region, the Atlantic Multidecadal Variability (AMV), as the missing link in the explanation of greening and browning trend patterns in the northern high latitudes. Such a link would also imply potential for decadal predictions of ecosystem changes in the northern high latitudes.</p><p>An analysis of observational and reanalysis data sets for the period 1979-2019 shows that locations characterized by greening trends largely coincide with warming summer temperature and increasing precipitation. Wherever either cooling or decreasing precipitation occurs, browning trends are observed over this period. These precipitation and temperature patterns are significantly correlated with a North Atlantic sea surface temperature index that represents the AMV signal, indicating its role in modulating greening/browning trend patterns in the northern high latitudes.</p><p>Using two large ensembles of coupled Earth system model simulations (100 members of MPI-ESM-LR Grand Ensemble and 32 members of the IPSL-CM6A-LR Large Ensemble), we separate the greening/browning pattern caused by external CO<sub>2</sub> forcing from that caused by internal climate variability associated with the AMV. These sets of model simulations enable a clean separation of the externally forced signal from internal variability. While the greening and browning patterns in the simulations do not agree with observations in terms of magnitude and location, we find consistent internally generated greening/browning patterns in both models caused by changes in temperature and precipitation linked to the AMV signal. These greening/browning trend patterns are of the same magnitude as those caused by the external forcing alone. Our work therefore shows that internally-generated changes of vegetation in the northern lands, driven by AMV, are potentially as large as those caused by external CO<sub>2</sub> forcing. We thus argue that the observed pattern of greening/browning in the northern high latitudes could originate from the combined effect of rising CO<sub>2</sub> as well as the AMV.</p>

scholarly journals Large-scale temperature response to external forcing in simulations and reconstructions of the last millennium

2013 ◽  
Vol 9 (1) ◽  
pp. 393-421 ◽  
Author(s):  
L. Fernández-Donado ◽  
J. F. González-Rouco ◽  
C. C. Raible ◽  
C. M. Ammann ◽  
D. Barriopedro ◽  
...  
Keyword(s):  
Internal Variability ◽  
Ice Age ◽  
Future Climate Change ◽  
Last Millennium ◽  
External Forcing ◽  
Transient Climate Response ◽  
Temperature Changes ◽  
Model Simulations ◽  
Major Player ◽  
Intercomparison Project

Abstract. Understanding natural climate variability and its driving factors is crucial to assessing future climate change. Therefore, comparing proxy-based climate reconstructions with forcing factors as well as comparing these with paleoclimate model simulations is key to gaining insights into the relative roles of internal versus forced variability. A review of the state of modelling of the climate of the last millennium prior to the CMIP5–PMIP3 (Coupled Model Intercomparison Project Phase 5–Paleoclimate Modelling Intercomparison Project Phase 3) coordinated effort is presented and compared to the available temperature reconstructions. Simulations and reconstructions broadly agree on reproducing the major temperature changes and suggest an overall linear response to external forcing on multidecadal or longer timescales. Internal variability is found to have an important influence at hemispheric and global scales. The spatial distribution of simulated temperature changes during the transition from the Medieval Climate Anomaly to the Little Ice Age disagrees with that found in the reconstructions. Thus, either internal variability is a possible major player in shaping temperature changes through the millennium or the model simulations have problems realistically representing the response pattern to external forcing. A last millennium transient climate response (LMTCR) is defined to provide a quantitative framework for analysing the consistency between simulated and reconstructed climate. Beyond an overall agreement between simulated and reconstructed LMTCR ranges, this analysis is able to single out specific discrepancies between some reconstructions and the ensemble of simulations. The disagreement is found in the cases where the reconstructions show reduced covariability with external forcings or when they present high rates of temperature change.

scholarly journals The Sensitivity of the Hydrological Cycle to Internal Climate Variability versus Anthropogenic Climate Change

2016 ◽  
Vol 29 (10) ◽  
pp. 3661-3673 ◽  
Author(s):  
Ryan J. Kramer ◽  
Brian J. Soden
Keyword(s):  
Water Vapor ◽  
Time Scales ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Internal Variability ◽  
Radiative Cooling ◽  
Surface Warming ◽  
Internal Climate Variability ◽  
Radiative Processes ◽  
Global Mean

Abstract In response to rising CO2 concentrations, climate models predict that globally averaged precipitation will increase at a much slower rate than water vapor. However, some observational studies suggest that global-mean precipitation and water vapor have increased at similar rates. While the modeling results emphasize changes at multidecadal time scales where the anthropogenic signal dominates, the shorter observational record is more heavily influenced by internal variability. Whether the physical constraints on the hydrological cycle fundamentally differ between these time scales is investigated. The results of this study show that while global-mean precipitation is constrained by radiative cooling on both time scales, the effects of CO2 dominate on multidecadal time scales, acting to suppress the increase in radiative cooling with warming. This results in a smaller precipitation change compared to interannual time scales where the effects of CO2 forcing are small. It is also shown that intermodel spread in the response of atmospheric radiative cooling (and thus global-mean precipitation) to anthropogenically forced surface warming is dominated by clear-sky radiative processes and not clouds, while clouds dominate under internal variability. The findings indicate that the sensitivity of the global hydrological cycle to surface warming differs fundamentally between internal variability and anthropogenically forced changes and this has important implications for interpreting observations of the hydrological sensitivity.

scholarly journals Attribution of Declining Western U.S. Snowpack to Human Effects

2008 ◽  
Vol 21 (23) ◽  
pp. 6425-6444 ◽  
Author(s):  
David W. Pierce ◽  
Tim P. Barnett ◽  
Hugo G. Hidalgo ◽  
Tapash Das ◽  
Céline Bonfils ◽  
...  
Keyword(s):  
Climate Variability ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Formal Model ◽  
Model Estimate ◽  
Economic Implications ◽  
Internal Climate Variability ◽  
Detection And Attribution

Abstract Observations show snowpack has declined across much of the western United States over the period 1950–99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D–A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean–atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D–A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.

scholarly journals A Method for Detection and Attribution of Regional Precipitation Change Using Granger Causality Application to the United States Historical Record

2021 ◽  
Author(s):  
Mark Risser ◽  
William Collins ◽  
Michael Wehner ◽  
Travis O'Brien ◽  
Christopher Paciorek ◽  
...  
Keyword(s):  
United States ◽  
Climate Models ◽  
Water Cycle ◽  
Global Climate ◽  
Internal Variability ◽  
The United States ◽  
Historical Record ◽  
Global Climate Models ◽  
Detection And Attribution ◽  
Observational Uncertainty

Abstract Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we propose a novel approach to regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is minimized. In a single framework, we simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. We use output from global climate models in a perfect-data sense to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day). In doing so, we turn an apparent limitation into an opportunity by using the diversity of responses to short-lived climate forcers across the CMIP6 multi-model ensemble to ensure our D&A is insensitive to structural uncertainty. Our framework is developed using a Pearl-causal perspective, but forthcoming research now underway will apply the framework to in situ measurements using a Granger-causal perspective. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

scholarly journals Optimal Detection of Regional Trends Using Global Data

2010 ◽  
Vol 23 (16) ◽  
pp. 4438-4446 ◽  
Author(s):  
Stephen S. Leroy ◽  
James G. Anderson
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Climate Models ◽  
Region Of Interest ◽  
Internal Variability ◽  
Spatiotemporal Patterns ◽  
Optimal Detection ◽  
Internal Climate Variability ◽  
Climate Signals ◽  
Normally Distributed

Abstract A complete accounting of model uncertainty in the optimal detection of climate signals requires normalization of the signals produced by climate models; however, there is not yet a well-defined rule for the normalization. This study seeks to discover such a rule. The authors find that, to arrive at the equations of optimal detection from a general application of Bayesian statistics to the problem of climate change, it is necessary to assume that 1) the prior probability density function (PDF) for climate change is separable into independent PDFs for sensitivity and the signals’ spatiotemporal patterns; 2) postfit residuals are due to internal variability and are normally distributed; 3) the prior PDF for sensitivity is uninformative; and 4) a continuum of climate models used to estimate model uncertainty gives a normally distributed PDF for the spatiotemporal patterns for the climate signals. This study also finds that the rule for normalization of the signals’ patterns is a simple division of model-simulated climate change in any observable quantity or set of quantities by a change in a single quantity of interest such as regionally averaged temperature or precipitation. With this normalization, optimal detection yields the most probable estimates of the underlying changes in the region of interest due to external forcings. Data outside the region of interest add information that effectively suppresses the interannual fluctuations associated with internal climate variability.

scholarly journals Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

2013 ◽  
Vol 26 (18) ◽  
pp. 6904-6914 ◽  
Author(s):  
David E. Rupp ◽  
Philip W. Mote ◽  
Nathaniel L. Bindoff ◽  
Peter A. Stott ◽  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Forcing ◽  
Detection And Attribution ◽  
Snow Cover Extent

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

scholarly journals Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

2021 ◽  
Vol 12 (2) ◽  
pp. 401-418
Author(s):  
Nicola Maher ◽  
Sebastian Milinski ◽  
Ralf Ludwig
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Internal Variability ◽  
Climate System ◽  
Single Type ◽  
Special Issue ◽  
Large Ensemble ◽  
Model Simulations ◽  
Different Types ◽  
Climate Model Simulations

Abstract. Single model initial-condition large ensembles (SMILEs) are valuable tools that can be used to investigate the climate system. SMILEs allow scientists to quantify and separate the internal variability of the climate system and its response to external forcing, with different types of SMILEs appropriate to answer different scientific questions. In this editorial we first provide an introduction to SMILEs and an overview of the studies in the special issue “Large Ensemble Climate Model Simulations: Exploring Natural Variability, Change Signals and Impacts”. These studies analyse a range of different types of SMILEs including global climate models (GCMs), regionally downscaled climate models (RCMs), a hydrological model with input from a RCM SMILE, a SMILE with prescribed sea surface temperature (SST) built for event attribution, a SMILE that assimilates observed data, and an initialised regional model. These studies provide novel methods, that can be used with SMILEs. The methods published in this issue include a snapshot empirical orthogonal function analysis used to investigate El Niño–Southern Oscillation teleconnections; the partitioning of future uncertainty into model differences, internal variability, and scenario choices; a weighting scheme for multi-model ensembles that can incorporate SMILEs; and a method to identify the required ensemble size for any given problem. Studies in this special issue also focus on RCM SMILEs, with projections of the North Atlantic Oscillation and its regional impacts assessed over Europe, and an RCM SMILE intercomparison. Finally a subset of studies investigate projected impacts of global warming, with increased water flows projected for future hydrometeorological events in southern Ontario; precipitation projections over central Europe are investigated and found to be inconsistent across models in the Alps, with a continuation of past tendencies in Mid-Europe; and equatorial Asia is found to have an increase in the probability of large fire and drought events under higher levels of warming. These studies demonstrate the utility of different types of SMILEs. In the second part of this editorial we provide a perspective on how three types of SMILEs could be combined to exploit the advantages of each. To do so we use a GCM SMILE and an RCM SMILE with all forcings, as well as a naturally forced GCM SMILE (nat-GCM) over the European domain. We utilise one of the key advantages of SMILEs, precisely separating the forced response and internal variability within an individual model to investigate a variety of simple questions. Broadly we show that the GCM can be used to investigate broad-scale patterns and can be directly compared to the nat-GCM to attribute forced changes to either anthropogenic emissions or volcanoes. The RCM provides high-resolution spatial information of both the forced change and the internal variability around this change at different warming levels. By combining all three ensembles we can gain information that would not be available using a single type of SMILE alone, providing a perspective on future research that could be undertaken using these tools.

scholarly journals Observed and Projected Changes to the Precipitation Annual Cycle

2017 ◽  
Vol 30 (13) ◽  
pp. 4983-4995 ◽  
Author(s):  
Kate Marvel ◽  
Michela Biasutti ◽  
Céline Bonfils ◽  
Karl E. Taylor ◽  
Yochanan Kushnir ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Internal Variability ◽  
Cycle Phase ◽  
External Forcing ◽  
Detection And Attribution ◽  
Global Mean Temperature ◽  
Attribution Analysis ◽  
Projected Changes ◽  
Global Mean

Anthropogenic climate change is predicted to cause spatial and temporal shifts in precipitation patterns. These may be apparent in changes to the annual cycle of zonal mean precipitation P. Trends in the amplitude and phase of the P annual cycle in two long-term, global satellite datasets are broadly similar. Model-derived fingerprints of externally forced changes to the amplitude and phase of the P seasonal cycle, combined with these observations, enable a formal detection and attribution analysis. Observed amplitude changes are inconsistent with model estimates of internal variability but not attributable to the model-predicted response to external forcing. This mismatch between observed and predicted amplitude changes is consistent with the sustained La Niña–like conditions that characterize the recent slowdown in the rise of the global mean temperature. However, observed changes to the annual cycle phase do not seem to be driven by this recent hiatus. These changes are consistent with model estimates of forced changes, are inconsistent (in one observational dataset) with estimates of internal variability, and may suggest the emergence of an externally forced signal.

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