scholarly journals Technical Note: The analogue method for millennial-scale, spatiotemporal climate reconstructions

2020 ◽  
Author(s):  
Oliver Bothe ◽  
Eduardo Zorita
Keyword(s):  
Similarity Criterion ◽  
Complete Information ◽  
Technical Note ◽  
Data Uncertainty ◽  
Spatial Covariance ◽  
Synchronous Sampling ◽  
Proxy Records ◽  
Climate Reconstructions ◽  
Climate Simulations ◽  
Analogue Method

Abstract. Inferences about climate states and climate variability of the Holocene and the deglaciation rely on sparse paleo-observational proxy data. Combining these sparse proxies with output from climate simulations is a means for increasing the understanding of the climate throughout the last ~ 21 millennia. The analogue method is one approach to do this. The method takes a number of sparse proxy records and then searches within a pool of more complete information (e.g., model simulations) for analogues according to a similarity criterion. The analogue method is non-linear and allows considering the spatial covariance among proxy records. Beyond the last two millennia, we have to rely on proxies that are not only sparse in space but also irregular in time and with considerably uncertain dating. This poses additional challenges for the analogue method, which have seldom been addressed previously. The method has to address the uncertainty of the proxy-inferred variables as well as the uncertain dating. It has to cope with the irregular and non-synchronous sampling of different proxies. Here, we propose a specific way of dealing with these obstacles. We use uncertainty ellipses for tuples of individual proxy values and dates and, thereby, consider the dating as well as the data uncertainty. Results highlight the potential of the method to reconstruct the climate of the last ~ 15 millennia. However, in the present case, the reconstructions show little variability of their central estimates but large uncertainty ranges. The reconstruction by analogue provides not only a regional average record but also allows assessing the climate state compliant with the used proxy predictors. These fields reveal that uncertainty are also large locally. Our results emphasize the ambiguity of reconstructions from spatially sparse and temporally uncertain, irregularly sampled proxies.

scholarly journals Technical note: Considerations on using uncertain proxies in the analogue method for spatiotemporal reconstructions of millennial-scale climate

2021 ◽  
Vol 17 (2) ◽  
pp. 721-751
Author(s):  
Oliver Bothe ◽  
Eduardo Zorita
Keyword(s):  
Similarity Criterion ◽  
Complete Information ◽  
Technical Note ◽  
Data Uncertainty ◽  
Time Dimension ◽  
Spatial Covariance ◽  
Synchronous Sampling ◽  
Proxy Records ◽  
Climate Simulations ◽  
Analogue Method

Abstract. Inferences about climate states and climate variability of the Holocene and the deglaciation rely on sparse paleo-observational proxy data. Combining these proxies with output from climate simulations is a means for increasing the understanding of the climate throughout the last tens of thousands of years. The analogue method is one approach to do this. The method takes a number of sparse proxy records and then searches within a pool of more complete information (e.g., model simulations) for analogues according to a similarity criterion. The analogue method is non-linear and allows considering the spatial covariance among proxy records. Beyond the last two millennia, we have to rely on proxies that are not only sparse in space but also irregular in time and with considerably uncertain dating. This poses additional challenges for the analogue method, which have seldom been addressed previously. The method has to address the uncertainty of the proxy-inferred variables as well as the uncertain dating. It has to cope with the irregular and non-synchronous sampling of different proxies. Here, we describe an implementation of the analogue method including a specific way of addressing these obstacles. We include the uncertainty in our proxy estimates by using “ellipses of tolerance” for tuples of individual proxy values and dates. These ellipses are central to our approach. They describe a region in the plane spanned by proxy dimension and time dimension for which a model analogue is considered to be acceptable. They allow us to consider the dating as well as the data uncertainty. They therefore form the basic criterion for selecting valid analogues. We discuss the benefits and limitations of this approach. The results highlight the potential of the analogue method to reconstruct the climate from the deglaciation up to the late Holocene. However, in the present case, the reconstructions show little variability of their central estimates but large uncertainty ranges. The reconstruction by analogue provides not only a regional average record but also allows assessing the spatial climate field compliant with the used proxy predictors. These fields reveal that uncertainties are also locally large. Our results emphasize the ambiguity of reconstructions from spatially sparse and temporally uncertain, irregularly sampled proxies.

scholarly journals Simple noise estimates and pseudoproxies for the last 21 000 years

2019 ◽  
Vol 11 (3) ◽  
pp. 1129-1152
Author(s):  
Oliver Bothe ◽  
Sebastian Wagner ◽  
Eduardo Zorita
Keyword(s):  
Noise Model ◽  
Climate Simulation ◽  
High Temporal Resolution ◽  
Model Code ◽  
Proxy Records ◽  
Climate Reconstructions ◽  
Noise Characteristics ◽  
Climate Simulations ◽  
Spatial Differences ◽  
Long Timescales

Abstract. Climate reconstructions are means to extract the signal from uncertain paleo-observations, so-called proxies. It is essential to evaluate these reconstructions to understand and quantify their uncertainties. Similarly, comparing climate simulations and proxies requires approaches to bridge the temporal and spatial differences between both and to address their specific uncertainties. One way to achieve these two goals is so-called pseudoproxies. These are surrogate proxy records within the virtual reality of a climate simulation. They in turn depend on an understanding of the uncertainties of the real proxies including the noise characteristics disturbing the original environmental signal. Common pseudoproxy approaches so far concentrate on data with high temporal resolution over the last approximately 2000 years. Here we provide a simple but flexible noise model for potentially low-resolution sedimentary climate proxies for temperature on millennial timescales, the code for calculating a set of pseudoproxies from a simulation, and one example of pseudoproxies. The noise model considers the influence of other environmental variables, a dependence on the climate state, a bias due to changing seasonality, modifications of the archive (for example bioturbation), potential sampling variability, and a measurement error. Model, code, and data allow us to develop new ways of comparing simulation data with proxies on long timescales. Code and data are available at https://doi.org/10.17605/OSF.IO/ZBEHX (Bothe et al., 2018).

scholarly journals Reconstructing the NH Mean Temperature: Can Underestimation of Trends and Variability Be Avoided?

2011 ◽  
Vol 24 (3) ◽  
pp. 674-692 ◽  
Author(s):  
Bo Christiansen
Keyword(s):  
Low Frequency ◽  
New Method ◽  
Temperature Fields ◽  
Reconstruction Method ◽  
Climate Reconstructions ◽  
Reconstruction Methods ◽  
Low Frequency Variability ◽  
Climate Simulations ◽  
Mean Climate ◽  
Geographical Locations

Abstract There are indications that hemispheric-mean climate reconstructions seriously underestimate the amplitude of low-frequency variability and trends. Some of the theory of linear regression and error-in-variables models is reviewed to identify the sources of this problem. On the basis of the insight gained, a reconstruction method that is supposed to minimize the underestimation is formulated. The method consists of reconstructing the local temperatures at the geographical locations of the proxies, followed by calculating the hemispheric average. The method is tested by applying it to an ensemble of surrogate temperature fields based on two climate simulations covering the last 500 and 1000 yr. Compared to the regularized expectation maximization (RegEM) truncated total least squares (TTLS) method and a composite-plus-scale method—two methods recently used in the literature—the new method strongly improves the behavior regarding low-frequency variability and trends. The potential importance in real-world situations is demonstrated by applying the methods to a set of 14 decadally smoothed proxies. Here the new method shows much larger low-frequency variability and a much colder preindustrial temperature level than the other reconstruction methods. However, this should mainly be seen as a demonstration of the potential losses and gains of variability, as the reconstructions based on the 14 decadally smoothed proxies are not very robust.

scholarly journals Review article "On the origins of decadal climate variability: a network perspective"

2012 ◽  
Vol 19 (5) ◽  
pp. 559-568 ◽  
Author(s):  
A. A. Tsonis ◽  
K. L. Swanson
Keyword(s):  
Collective Behavior ◽  
Decadal Variability ◽  
Review Article ◽  
Decadal Climate Variability ◽  
Synchronous State ◽  
Proxy Records ◽  
Climate Simulations ◽  
Decadal Climate ◽  
Instrumental Records ◽  
Climate Networks

Abstract. This review is a synthesis of work spanning the last 25 yr. It is largely based on the use of climate networks to identify climate subsystems/major modes and to subsequently study how their collective behavior explains decadal variability. The central point is that a network of coupled nonlinear subsystems may at times begin to synchronize. If during synchronization the coupling between the subsystems increases, the synchronous state may, at some coupling strength threshold, be destroyed shifting climate to a new regime. This climate shift manifests itself as a change in global temperature trend. This mechanism, which is consistent with the theory of synchronized chaos, appears to be a very robust mechanism of the climate system. It is found in the instrumental records, in forced and unforced climate simulations, as well as in proxy records spanning several centuries.

scholarly journals Pace, magnitude, and nature of terrestrial climate change through the end-Permian extinction in southeastern Gondwana

Geology ◽  
2021 ◽  
Author(s):  
T.D. Frank ◽  
C.R. Fielding ◽  
A.M.E. Winguth ◽  
K. Savatic ◽  
A. Tevyaw ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Chemical Weathering ◽  
Regional Climate ◽  
Proxy Records ◽  
Climate Simulations ◽  
Southeastern Margin ◽  
Terrestrial Environments ◽  
Marine Realm ◽  
Permian Extinction

Rapid climate change was a major contributor to the end-Permian extinction (EPE). Although well constrained for the marine realm, relatively few records document the pace, nature, and magnitude of climate change across the EPE in terrestrial environments. We generated proxy records for chemical weathering and land surface temperature from continental margin deposits of the high-latitude southeastern margin of Gondwana. Regional climate simulations provide additional context. Results show that Glossopteris forest-mire ecosystems collapsed during a pulse of intense chemical weathering and peak warmth, which capped ~1 m.y. of gradual warming and intensification of seasonality. Erosion resulting from loss of vegetation was short lived in the low-relief landscape. Earliest Triassic climate was ~10–14 °C warmer than the late Lopingian and landscapes were no longer persistently wet. Aridification, commonly linked to the EPE, developed gradually, facilitating the persistence of refugia for moisture-loving terrestrial groups.

scholarly journals Sedproxy: a forward model for sediment archived climate proxies

2018 ◽  
Author(s):  
Andrew M. Dolman ◽  
Thomas Laepple
Keyword(s):  
R Package ◽  
Forward Model ◽  
Recorded Signal ◽  
Proxy Records ◽  
Climate Reconstructions ◽  
Reconstruction Methods ◽  
Geochemical Parameters ◽  
Many Sources ◽  
User Friendly ◽  
Climate Proxies

Abstract. Climate reconstructions based on proxy records recovered from marine sediments, such as alkenone records or geochemical parameters measured on foraminifera, play an important role in our understanding of the climate system. They provide information about the state of the ocean ranging back hundreds to millions of years and form the backbone of paleo-oceanography. However, there are many sources of uncertainty associated with the signal recovered from sediment archived proxies. These include seasonal or depth habitat biases in the recorded signal, a frequency dependent reduction in the amplitude of the recorded signal due to bioturbation of the sediment, aliasing of high frequency climate variation onto a nominally annual, decadal or centennial resolution signal, and additional sample processing and measurement error introduced when the proxy signal is recovered. Here we present a forward model for sediment archived proxies that jointly models the above processes, so that the magnitude of their separate and combined effects can be investigated. Applications include the interpretation and analysis of uncertainty in existing proxy records, parameter sensitivity analysis to optimize future studies, and the generation of pseudo-proxy records that can be used to test reconstruction methods. We provide examples, such as the simulation of individual foraminifera records, that demonstrate the usefulness of the forward model for paleoclimate studies. The model is implemented as a user-friendly R package, sedproxy, the use of which we hope will contribute to a better understanding of both the limitations and potential of marine sediment proxies to inform about past climate.

scholarly journals Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions

2010 ◽  
Vol 6 (2) ◽  
pp. 273-279 ◽  
Author(s):  
C. M. Ammann ◽  
M. G. Genton ◽  
B. Li
Keyword(s):  
Climate Models ◽  
Ice Cores ◽  
Mean Amplitude ◽  
Temperature Reconstruction ◽  
Technical Note ◽  
Data Series ◽  
Climate Reconstructions ◽  
Amplitude Attenuation ◽  
The Impact ◽  
Change In Mean

Abstract. Regression-based climate reconstructions scale one or more noisy proxy records against a (generally) short instrumental data series. Based on that relationship, the indirect information is then used to estimate that particular measure of climate back in time. A well-calibrated proxy record(s), if stationary in its relationship to the target, should faithfully preserve the mean amplitude of the climatic variable. However, it is well established in the statistical literature that traditional regression parameter estimation can lead to substantial amplitude attenuation if the predictors carry significant amounts of noise. This issue is known as "Measurement Error" (Fuller, 1987; Carroll et al., 2006). Climate proxies derived from tree-rings, ice cores, lake sediments, etc., are inherently noisy and thus all regression-based reconstructions could suffer from this problem. Some recent applications attempt to ward off amplitude attenuation, but implementations are often complex (Lee et al., 2008) or require additional information, e.g. from climate models (Hegerl et al., 2006, 2007). Here we explain the cause of the problem and propose an easy, generally applicable, data-driven strategy to effectively correct for attenuation (Fuller, 1987; Carroll et al., 2006), even at annual resolution. The impact is illustrated in the context of a Northern Hemisphere mean temperature reconstruction. An inescapable trade-off for achieving an unbiased reconstruction is an increase in variance, but for many climate applications the change in mean is a core interest.

scholarly journals Assimilating monthly precipitation data in a paleoclimate data assimilation framework

2020 ◽  
Vol 16 (4) ◽  
pp. 1309-1323
Author(s):  
Veronika Valler ◽  
Yuri Brugnara ◽  
Jörg Franke ◽  
Stefan Brönnimann
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Level Pressure ◽  
Proxy Records ◽  
Climate Reconstructions ◽  
Data Source

Abstract. Data assimilation approaches such as the ensemble Kalman filter method have become an important technique for paleoclimatological reconstructions and reanalysis. Different sources of information, from proxy records and documentary data to instrumental measurements, were assimilated in previous studies to reconstruct past climate fields. However, precipitation reconstructions are often based on indirect sources (e.g., proxy records). Assimilating precipitation measurements is a challenging task because they have high uncertainties, often represent only a small region, and generally do not follow a Gaussian distribution. In this paper, experiments are conducted to test the possibility of using information about precipitation in climate reconstruction with monthly resolution by assimilating monthly instrumental precipitation amounts or the number of wet days per month, solely or in addition to other climate variables such as temperature and sea-level pressure, into an ensemble of climate model simulations. The skill of all variables (temperature, precipitation, sea-level pressure) improved over the pure model simulations when only monthly precipitation amounts were assimilated. Assimilating the number of wet days resulted in similar or better skill compared to assimilating the precipitation amount. The experiments with different types of instrumental observations being assimilated indicate that precipitation data can be useful, particularly if no other variable is available from a given region. Overall the experiments show promising results because with the assimilation of precipitation information a new data source can be exploited for climate reconstructions. The wet day records can become an especially important data source in future climate reconstructions because many existing records date several centuries back in time and are not limited by the availability of meteorological instruments.

scholarly journals Pseudo-proxy tests of the analogue method to reconstruct spatially resolved global temperature during the Common Era

2017 ◽  
Vol 13 (6) ◽  
pp. 629-648 ◽  
Author(s):  
Juan José Gómez-Navarro ◽  
Eduardo Zorita ◽  
Christoph C. Raible ◽  
Raphael Neukom
Keyword(s):  
Missing Values ◽  
Real Space ◽  
Empirical Orthogonal Functions ◽  
Test Bed ◽  
Orthogonal Functions ◽  
Proxy Records ◽  
Spatially Resolved ◽  
Analogue Method ◽  
The Common ◽  
Common Era

Abstract. This study addresses the possibility of carrying out spatially resolved global reconstructions of annual mean temperature using a worldwide network of proxy records and a method based on the search of analogues. Several variants of the method are evaluated, and their performance is analysed. As a test bed for the reconstruction, the PAGES 2k proxy database (version 1.9.0) is employed as a predictor, the HadCRUT4 dataset is the set of observations used as the predictand and target, and a set of simulations from the PMIP3 simulations are used as a pool to draw analogues and carry out pseudo-proxy experiments (PPEs). The performance of the variants of the analogue method (AM) is evaluated through a series of PPEs in growing complexity, from a perfect-proxy scenario to a realistic one where the pseudo-proxy records are contaminated with noise (white and red) and missing values, mimicking the limitations of actual proxies. Additionally, the method is tested by reconstructing the real observed HadCRUT4 temperature based on the calibration of real proxies. The reconstructed fields reproduce the observed decadal temperature variability. From all the tests, we can conclude that the analogue pool provided by the PMIP3 ensemble is large enough to reconstruct global annual temperatures during the Common Era. Furthermore, the search of analogues based on a metric that minimises the RMSE in real space outperforms other evaluated metrics, including the search of analogues in the range-reduced space expanded by the leading empirical orthogonal functions (EOFs). These results show how the AM is able to spatially extrapolate the information of a network of local proxy records to produce a homogeneous gap-free climate field reconstruction with valuable information in areas barely covered by proxies and make the AM a suitable tool to produce valuable climate field reconstructions for the Common Era.

scholarly journals Sedproxy: a forward model for sediment-archived climate proxies

2018 ◽  
Vol 14 (12) ◽  
pp. 1851-1868 ◽  
Author(s):  
Andrew M. Dolman ◽  
Thomas Laepple
Keyword(s):  
R Package ◽  
Forward Model ◽  
Recorded Signal ◽  
Proxy Records ◽  
Climate Reconstructions ◽  
Reconstruction Methods ◽  
Geochemical Parameters ◽  
Many Sources ◽  
Climate Proxies ◽  
Additional Sample

Abstract. Climate reconstructions based on proxy records recovered from marine sediments, such as alkenone records or geochemical parameters measured on foraminifera, play an important role in our understanding of the climate system. They provide information about the state of the ocean ranging back hundreds to millions of years and form the backbone of paleo-oceanography. However, there are many sources of uncertainty associated with the signal recovered from sediment-archived proxies. These include seasonal or depth-habitat biases in the recorded signal; a frequency-dependent reduction in the amplitude of the recorded signal due to bioturbation of the sediment; aliasing of high-frequency climate variation onto a nominally annual, decadal, or centennial resolution signal; and additional sample processing and measurement error introduced when the proxy signal is recovered. Here we present a forward model for sediment-archived proxies that jointly models the above processes so that the magnitude of their separate and combined effects can be investigated. Applications include the interpretation and analysis of uncertainty in existing proxy records, parameter sensitivity analysis to optimize future studies, and the generation of pseudo-proxy records that can be used to test reconstruction methods. We provide examples, such as the simulation of individual foraminifera records, that demonstrate the usefulness of the forward model for paleoclimate studies. The model is implemented as an open-source R package, sedproxy, to which we welcome collaborative contributions. We hope that use of sedproxy will contribute to a better understanding of both the limitations and potential of marine sediment proxies to inform researchers about earth's past climate.

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