scholarly journals Empirical estimate of the signal content of Holocene temperature proxy records

2019 ◽  
Vol 15 (2) ◽  
pp. 521-537 ◽  
Author(s):  
Maria Reschke ◽  
Kira Rehfeld ◽  
Thomas Laepple
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Model Simulation ◽  
Correlation Structure ◽  
Past Climate ◽  
Time Uncertainty ◽  
Proxy Records ◽  
Spatial Correlation Structure ◽  
Temperature Proxy

Abstract. Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate-related effects as well as time uncertainty. As proxy-based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy record signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the middle to late Holocene (last 6000 years). This is achieved through a comparison of the correlation of proxy time series from nearby sites of three compilations and model time series extracted at the proxy sites from two transient climate model simulations: a Holocene simulation of the ECHAM5/MPI-OM model and the Holocene part of the TraCE-21ka simulation. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial timescales to be low (R<0.2), even for nearby sites, which resulted in low SNR estimates. The estimated SNRs depend on the assumed time uncertainty of the proxy records, the timescale analysed, and the model simulation used. Using the spatial correlation structure of the ECHAM5/MPI-OM simulation, the estimated SNRs on centennial timescales ranged from 0.05 – assuming no time uncertainty – to 0.5 for a time uncertainty of 400 years. On millennial timescales, the estimated SNRs were generally higher. Use of the TraCE-21ka correlation structure generally resulted in lower SNR estimates than for ECHAM5/MPI-OM. As the number of available high-resolution proxy records continues to grow, a more detailed analysis of the signal content of specific proxy types should become feasible in the near future. The estimated low signal content of Holocene temperature compilations should caution against over-interpretation of these multi-proxy and multisite syntheses until further studies are able to facilitate a better characterisation of the signal content in paleoclimate records.

scholarly journals Empirical estimate of the signal content of Holocene temperature proxy records

2018 ◽  
Author(s):  
Maria Reschke ◽  
Kira Rehfeld ◽  
Thomas Laepple
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Time Series Data ◽  
Series Data ◽  
Past Climate ◽  
Proxy Records ◽  
Model Time Series ◽  
Climate Model Simulations ◽  
Temperature Proxy

Abstract. Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate related effects as well as time uncertainty. As proxy based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy records’ signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the mid to late Holocene. This is achieved through a comparison of proxy time series from close-by sites of three compilations and model time series data at the proxy sites from two transient Holocene climate model simulations. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial time scales to be rather low (R 

Influence of Ocean and Atmosphere Components on Simulated Climate Sensitivities

2013 ◽  
Vol 26 (1) ◽  
pp. 231-245 ◽  
Author(s):  
Michael Winton ◽  
Alistair Adcroft ◽  
Stephen M. Griffies ◽  
Robert W. Hallberg ◽  
Larry W. Horowitz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Model Simulation ◽  
Coupled Model ◽  
Ocean Model ◽  
Historical Simulation ◽  
Model Version ◽  
Salinity Response ◽  
The Impact

Abstract The influence of alternative ocean and atmosphere subcomponents on climate model simulation of transient sensitivities is examined by comparing three GFDL climate models used for phase 5 of the Coupled Model Intercomparison Project (CMIP5). The base model ESM2M is closely related to GFDL’s CMIP3 climate model version 2.1 (CM2.1), and makes use of a depth coordinate ocean component. The second model, ESM2G, is identical to ESM2M but makes use of an isopycnal coordinate ocean model. The authors compare the impact of this “ocean swap” with an “atmosphere swap” that produces the GFDL Climate Model version 3 (CM3) by replacing the AM2 atmospheric component with AM3 while retaining a depth coordinate ocean model. The atmosphere swap is found to have much larger influence on sensitivities of global surface temperature and Northern Hemisphere sea ice cover. The atmosphere swap also introduces a multidecadal response time scale through its indirect influence on heat uptake. Despite significant differences in their interior ocean mean states, the ESM2M and ESM2G simulations of these metrics of climate change are very similar, except for an enhanced high-latitude salinity response accompanied by temporarily advancing sea ice in ESM2G. In the ESM2G historical simulation this behavior results in the establishment of a strong halocline in the subpolar North Atlantic during the early twentieth century and an associated cooling, which are counter to observations in that region. The Atlantic meridional overturning declines comparably in all three models.

Quantification of terrestrial regional climate variability from a large database of pollen-based reconstructions and implications for future projections

2020 ◽  
Author(s):  
Raphael Hébert ◽  
Ulrike herzschuh ◽  
Thomas Laepple
Keyword(s):  
Climate Variability ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Irregular Sampling ◽  
Large Database ◽  
Spatial Correlation Structure ◽  
Pollen Records

&lt;p&gt;Multidecadal to millenial timescale climate variability has been investigated over the ocean&lt;/p&gt;&lt;p&gt;using extensive proxy data and it was found to yield coherent interproxy estimates of global and regional sea-surface temperature (SST) climate variability (Laepple and Huybers, 2014). Global Climate Model (GCM) simulations on the other hand, were found to exhibit an increasingly large deficit of regional SST climate variability for increasingly longer timescales.&lt;/p&gt;&lt;p&gt;Further investigation is needed to better quantify terrestrial climate variability for long&lt;/p&gt;&lt;p&gt;timescales and validate climate models.&lt;/p&gt;&lt;p&gt;Vegetation related proxies such as tree rings and pollen records are the most widespread&lt;/p&gt;&lt;p&gt;types of archives available to investigate terrestrial climate variability. Tree ring records are&lt;/p&gt;&lt;p&gt;particularly useful for short time scales estimates due to their annual resolution, while pollen-based reconstructions are necessary to cover the longer timescales. In the present work, we use a large database of 1873 pollen records covering the northern hemisphere in order to quantify Holocene vegetation and climate variability for the first time at centennial to multi-millenial timescales.&lt;/p&gt;&lt;p&gt;To ensure the robustness of our results, we are particularly interested in the spatio-temporal representativity of the archived signal in pollen records after taking into account the effective spatial scale, the intermittent and irregular sampling, the age-uncertainty and the sediment mixing effect. A careful treatment of the proxy formation allows us to investigate the spatial correlation structure of the pollen-based climate reconstructions as a function of timescales. The pollen data results are then contrasted with the analysis replicated using transient Holocene simulations produced with state-of-the-art climate models as well as stochastic climate model simulations.Our results indicate a substantial gap in terrestrial climate variability between the climate model simulations and the pollen reconstructions at centennial to multi-millenial timescales, mirroring the variability gap found in the marine domain. Finally, we investigate how future climate model projections with greater internal variability would be affected, and how this increases the uncertainty of regional land temperature projections.&lt;/p&gt;

scholarly journals Deriving user-informed climate information from climate model ensemble results

2017 ◽  
Vol 14 ◽  
pp. 261-269 ◽  
Author(s):  
Heike Huebener ◽  
Peter Hoffmann ◽  
Klaus Keuler ◽  
Susanne Pfeifer ◽  
Hans Ramthun ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
Research Output ◽  
Model Simulation ◽  
Regional Climate ◽  
Regional Climate Modeling ◽  
Climate Impact ◽  
Final Project ◽  
User Workshop

Abstract. Communication between providers and users of climate model simulation results still needs to be improved. In the German regional climate modeling project ReKliEs-De a midterm user workshop was conducted to allow the intended users of the project results to assess the preliminary results and to streamline the final project results to their needs. The user feedback highlighted, in particular, the still considerable gap between climate research output and user-tailored input for climate impact research. Two major requests from the user community addressed the selection of sub-ensembles and some condensed, easy to understand information on the strengths and weaknesses of the climate models involved in the project.

scholarly journals The influence of non-stationary ENSO teleconnections on reconstructions of paleoclimate using a pseudoproxy framework

2015 ◽  
Vol 11 (4) ◽  
pp. 3853-3895 ◽  
Author(s):  
R. Batehup ◽  
S. McGregor ◽  
A. J. E. Gallant
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Proxy Records ◽  
Enso Teleconnections ◽  
Reconstruction Methods ◽  
Paleoclimate Records ◽  
Proxy Reconstructions

Abstract. Reconstructions of the El Niño-Southern Oscillation (ENSO) ideally require high-quality, annually-resolved and long-running paleoclimate proxy records in the eastern tropical Pacific Ocean, located in ENSO's centre-of-action. However, to date, the paleoclimate records that have been extracted in the region are short or temporally and spatially sporadic, limiting the information that can be provided by these reconstructions. Consequently, most ENSO reconstructions exploit the downstream influences of ENSO on remote locations, known as teleconnections, where longer records from paleoclimate proxies exist. However, using teleconnections to reconstruct ENSO relies on the assumption that the relationship between ENSO and the remote location is stationary in time. Increasing evidence from observations and climate models suggests that some teleconnections are, in fact, non-stationary, potentially threatening the validity of those paleoclimate reconstructions that exploit teleconnections. This study examines the implications of non-stationary teleconnections on modern multi-proxy reconstructions of ENSO. The sensitivity of the reconstructions to non-stationary teleconnections were tested using a suite of idealized pseudoproxy experiments that employed output from a fully coupled global climate model. Reconstructions of the variance in the Niño 3.4 index, representing ENSO variability, were generated using four different methods to which surface temperature data from the GFDL CM2.1 was applied as a pseudoproxy. As well as sensitivity of the reconstruction to the method, the experiments tested the sensitivity of the reconstruction to the number of non-stationary pseudoproxies and the location of these proxies. ENSO reconstructions in the pseudoproxy experiments were not sensitive to non-stationary teleconnections when global, uniformly-spaced networks of a minimum of approximately 20 proxies were employed. Neglecting proxies from ENSO's center-of-action still produced skillful reconstructions, but the chance of generating a skillful reconstruction decreased. Reconstruction methods that utilized raw time series were the most sensitive to non-stationary teleconnections, while calculating the running variance of pseudoproxies first, appeared to improve the robustness of the resulting reconstructions. The results suggest that caution should be taken when developing reconstructions using proxies from a single teleconnected region, or those that use less than 20 source proxies.

scholarly journals Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

2020 ◽  
Vol 24 (5) ◽  
pp. 2817-2839
Author(s):  
Eric Pohl ◽  
Christophe Grenier ◽  
Mathieu Vrac ◽  
Masa Kageyama
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Natural Variability ◽  
Climate Signal ◽  
Lena River ◽  
River Catchment ◽  
Model Simulations ◽  
Non Parametric

Abstract. Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov–Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent. On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50 %–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools (last access: 19 May 2020).

Gaussian Process Regression – A tool for improved climate index reconstructions

2021 ◽  
Author(s):  
Marlene Klockmann ◽  
Eduardo Zorita
Keyword(s):  
Climate Variability ◽  
Gaussian Process ◽  
Climate Model ◽  
Model Simulation ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Climate Index ◽  
Test Environment ◽  
Past Climate ◽  
Noisy Input

&lt;p&gt;&lt;span&gt;We present a flexible non-linear framework of Gaussian Process Regression (GPR) for the reconstruction of past climate indexes such as the Atlantic Multidecadal Variability (AMV). These reconstructions are needed because the historical observation period is too short to provide a long-term perspective on climate variability. Climate indexes can be reconstructed from proxy data (e.g. tree rings) with the help of statistical models. Previous reconstructions of climate indexes mostly used some form of linear regression methods, which are known to underestimate the true amplitude of variability and perform poorly if noisy input data is used. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;We implement the machine-learning method GPR for climate index reconstruction with the goal of preserving the amplitude of past climate variability. To test the framework in a controlled environment, we create pseudo-proxies from a coupled climate model simulation of the past 2000 years. In our test environment, the GPR strongly improves the reconstruction of the AMV with respect to a multi-linear Principal Component Regression. The amplitude of reconstructed variability is very close to the true variability even if non-climatic noise is added to the pseudo-proxies. In addition, the framework can directly take into account known proxy uncertainties and fit data-sets with a variable number of records in time. Thus, the GPR framework seems to be a highly suitable tool for robust and improved climate index reconstructions.&lt;/span&gt;&lt;/p&gt;

scholarly journals Mid-to-late Holocene temperature evolution and atmospheric dynamics over Europe in regional model simulations

2016 ◽  
Vol 12 (8) ◽  
pp. 1645-1662 ◽  
Author(s):  
Emmanuele Russo ◽  
Ulrich Cubasch
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Late Holocene ◽  
Climate Models ◽  
Climate Model ◽  
Set Covering ◽  
Data Set ◽  
Near Surface ◽  
Past Climate ◽  
Past Climate Change

Abstract. The improvement in resolution of climate models has always been mentioned as one of the most important factors when investigating past climatic conditions, especially in order to evaluate and compare the results against proxy data. Despite this, only a few studies have tried to directly estimate the possible advantages of highly resolved simulations for the study of past climate change. Motivated by such considerations, in this paper we present a set of high-resolution simulations for different time slices of the mid-to-late Holocene performed over Europe using the state-of-the-art regional climate model COSMO-CLM. After proposing and testing a model configuration suitable for paleoclimate applications, the aforementioned mid-to-late Holocene simulations are compared against a new pollen-based climate reconstruction data set, covering almost all of Europe, with two main objectives: testing the advantages of high-resolution simulations for paleoclimatic applications, and investigating the response of temperature to variations in the seasonal cycle of insolation during the mid-to-late Holocene. With the aim of giving physically plausible interpretations of the mismatches between model and reconstructions, possible uncertainties of the pollen-based reconstructions are taken into consideration. Focusing our analysis on near-surface temperature, we can demonstrate that concrete advantages arise in the use of highly resolved data for the comparison against proxy-reconstructions and the investigation of past climate change. Additionally, our results reinforce previous findings showing that summertime temperatures during the mid-to-late Holocene were driven mainly by changes in insolation and that the model is too sensitive to such changes over Southern Europe, resulting in drier and warmer conditions. However, in winter, the model does not correctly reproduce the same amplitude of changes evident in the reconstructions, even if it captures the main pattern of the pollen data set over most of the domain for the time periods under investigation. Through the analysis of variations in atmospheric circulation we suggest that, even though the wintertime discrepancies between the two data sets in some areas are most likely due to high pollen uncertainties, in general the model seems to underestimate the changes in the amplitude of the North Atlantic Oscillation, overestimating the contribution of secondary modes of variability.

scholarly journals Towards high-resolution climate reconstruction using an off-line data assimilation and COSMO-CLM 5.00 model

2018 ◽  
Vol 14 (9) ◽  
pp. 1345-1360
Author(s):  
Bijan Fallah ◽  
Emmanuele Russo ◽  
Walter Acevedo ◽  
Achille Mauri ◽  
Nico Becker ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Climate Models ◽  
Climate Model ◽  
Chaotic Behavior ◽  
Initial Conditions ◽  
Estimation Error ◽  
Time Step ◽  
Proxy Records ◽  
Geographical Regions

Abstract. Data assimilation (DA) methods have been used recently to constrain the climate model forecasts by paleo-proxy records. Both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the time step of consequence for observations is usually too long for a dynamical model to follow the previous analysis state and the chaotic behavior of the model becomes dominant. The majority of recent paleoclimate studies using DA have performed low- or intermediate-resolution global simulations along with an “off-line” DA approach. In an off-line DA, the re-initialization cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo-observations and real observations into an ensemble of COSMO-CLM high-resolution regional climate model (RCM) simulations over Europe, for which the ensemble members slightly differ in boundary and initial conditions. Within a perfect model experiment, the performance of the applied DA scheme is evaluated with respect to its sensitivity to the noise levels of pseudo-observations. It was observed that the injected bias in the pseudo-observations linearly impacts the DA skill. Such experiments can serve as a tool for the selection of proxy records, which can potentially reduce the state estimation error when they are assimilated. Additionally, the sensitivity of COSMO-CLM to the boundary conditions is addressed. The geographical regions where the model exhibits high internal variability are identified. Two sets of experiments are conducted by averaging the observations over summer and winter. Furthermore, the effect of the spurious correlations within the observation space is studied and a optimal correlation radius, within which the observations are assumed to be correlated, is detected. Finally, the pollen-based reconstructed quantities at the mid-Holocene are assimilated into the RCM and the performance is evaluated against a test dataset. We conclude that the DA approach is a promising tool for creating high-resolution yearly analysis quantities. The affordable DA method can be applied to efficiently improve climate field reconstruction efforts by combining high-resolution paleoclimate simulations and the available proxy records.

scholarly journals Probabilistic Concepts in Intermediate-Complexity Climate Models: A Snapshot Attractor Picture

2015 ◽  
Vol 29 (1) ◽  
pp. 259-272 ◽  
Author(s):  
Mátyás Herein ◽  
János Márfy ◽  
Gábor Drótos ◽  
Tamás Tél
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Internal Variability ◽  
Co2 Concentration ◽  
Ensemble Average ◽  
Climate Projections ◽  
Intermediate Complexity ◽  
The North

Abstract A time series resulting from a single initial condition is shown to be insufficient for quantifying the internal variability in a climate model, and thus one is unable to make meaningful climate projections based on it. The authors argue that the natural distribution, obtained from an ensemble of trajectories differing solely in their initial conditions, of the snapshot attractor corresponding to a particular forcing scenario should be determined in order to quantify internal variability and to characterize any instantaneous state of the system in the future. Furthermore, as a simple measure of internal variability of any particular variable of the model, the authors suggest using its instantaneous ensemble standard deviation. These points are illustrated with the intermediate-complexity climate model Planet Simulator forced by a CO2 scenario, with a 40-member ensemble. In particular, the leveling off of the time dependence of any ensemble average is shown to provide a much clearer indication of reaching a steady state than any property of single time series. Shifts in ensemble averages are indicative of climate changes. The dynamical character of such changes is illustrated by hysteresis-like curves obtained by plotting the ensemble average surface temperature versus the CO2 concentration. The internal variability is found to be the most pronounced on small geographical scales. The traditionally used 30-yr temporal averages are shown to be considerably different from the corresponding ensemble averages. Finally, the North Atlantic Oscillation (NAO) index, related to the teleconnection paradigm, is also investigated. It is found that the NAO time series strongly differs in any individual realization from each other and from the ensemble average, and climatic trends can be extracted only from the latter.

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