Pseudo-proxy evaluation of climate field reconstruction methods of North Atlantic climate based on an annually resolved marine proxy network

Abstract. Two statistical methods are tested to reconstruct the interannual variations in past sea surface temperatures (SSTs) of the North Atlantic (NA) Ocean over the past millennium based on annually resolved and absolutely dated marine proxy records of the bivalve mollusk Arctica islandica. The methods are tested in a pseudo-proxy experiment (PPE) setup using state-of-the-art climate models (CMIP5 Earth system models) and reanalysis data from the COBE2 SST data set. The methods were applied in the virtual reality provided by global climate simulations and reanalysis data to reconstruct the past NA SSTs using pseudo-proxy records that mimic the statistical characteristics and network of Arctica islandica. The multivariate linear regression methods evaluated here are principal component regression and canonical correlation analysis. Differences in the skill of the climate field reconstruction (CFR) are assessed according to different calibration periods and different proxy locations within the NA basin. The choice of the climate model used as a surrogate reality in the PPE has a more profound effect on the CFR skill than the calibration period and the statistical reconstruction method. The differences between the two methods are clearer for the MPI-ESM model due to its higher spatial resolution in the NA basin. The pseudo-proxy results of the CCSM4 model are closer to the pseudo-proxy results based on the reanalysis data set COBE2. Conducting PPEs using noise-contaminated pseudo-proxies instead of noise-free pseudo-proxies is important for the evaluation of the methods, as more spatial differences in the reconstruction skill are revealed. Both methods are appropriate for the reconstruction of the temporal evolution of the NA SSTs, even though they lead to a great loss of variance away from the proxy sites. Under reasonable assumptions about the characteristics of the non-climate noise in the proxy records, our results show that the marine network of Arctica islandica can be used to skillfully reconstruct the spatial patterns of SSTs at the eastern NA basin.

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Pseudo-proxy evaluation of Climate Field Reconstruction methods of North Atlantic climate based on an annually resolved marine proxy network

10.5194/cp-2017-61 ◽

2017 ◽

Author(s):

Maria Pyrina ◽

Sebastian Wagner ◽

Eduardo Zorita

Keyword(s):

North Atlantic ◽

Principal Component Regression ◽

Principal Component ◽

Reanalysis Data ◽

Arctica Islandica ◽

Data Set ◽

Field Reconstruction ◽

The Past ◽

Proxy Records ◽

Climate Field Reconstruction

Abstract. Two statistical methods are tested to reconstruct the inter-annual variations of past sea surface temperatures (SSTs) of the North Atlantic (NA) Ocean over the past millennium, based on annually resolved and absolutely dated marine proxy records of the bivalve mollusk Arctica islandica. The methods are tested in a pseudo-proxy experiment (PPE) set-up using state-of-the-art climate models (CMIP5 Earth System Models) and reanalysis data from the COBE2 SST data set. The methods were applied in the virtual reality provided by global climate simulations and reanalysis data to reconstruct the past NA SSTs, using pseudoproxy records that mimic the statistical characteristics and network of Arctica islandica. The multivariate linear regression methods evaluated here are Principal Component Regression and Canonical Correlation Analysis. Differences in the skill of the Climate Field Reconstruction (CFR) are assessed according to different calibration periods and different proxy locations within the NA basin. The choice of the climate model used as surrogate reality in the PPE has a more profound effect on the CFR skill than the calibration period and the statistical reconstruction method. The differences between the two methods are clearer for the MPI-ESM model, due to its higher spatial resolution in the NA basin. The pseudo-proxy results of the CCSM4 model are closer to the pseudo-proxy results based on the reanalysis data set COBE2. The addition of noise in the pseudo-proxies is important for the evaluation of the methods, as more spatial differences in the reconstruction skill are revealed. More profound differences between methods are obtained when the number of proxy records is smaller than five, making the Principal Component Regression a more appropriate method in this case. Despite the differences, the results show that the marine network of Arctica islandica can be used to skilfully reconstruct the spatial patterns of SSTs at the eastern NA basin.

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Constraining two climate field reconstruction methodologies over the North Atlantic realm using pseudo-proxy experiments

Quaternary Science Reviews ◽

10.1016/j.quascirev.2021.107009 ◽

2021 ◽

Vol 265 ◽

pp. 107009

Author(s):

Tine Nilsen ◽

Stefanie Talento ◽

Johannes P. Werner

Keyword(s):

North Atlantic ◽

Field Reconstruction ◽

The North ◽

The North Atlantic ◽

Climate Field Reconstruction

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European wind variability over 140 yr

Advances in Science and Research ◽

10.5194/asr-10-51-2013 ◽

2013 ◽

Vol 10 (1) ◽

pp. 51-58 ◽

Cited By ~ 21

Author(s):

P. E. Bett ◽

H. E. Thornton ◽

R. T. Clark

Keyword(s):

Full Range ◽

Reanalysis Data ◽

Data Set ◽

Wind Speeds ◽

The Past ◽

North East ◽

The North ◽

Uncertainty Estimates ◽

Mean Wind Speed

Abstract. We present initial results of a study on the variability of wind speeds across Europe over the past 140 yr, making use of the recent Twentieth Century Reanalysis data set, which includes uncertainty estimates from an ensemble method of reanalysis. Maps of the means and standard deviations of daily wind speeds, and the Weibull-distribution parameters, show the expected features, such as the strong, highly-variable wind in the north-east Atlantic. We do not find any clear, strong long-term trends in wind speeds across Europe, and the variability between decades is large. We examine how different years and decades are related in the long-term context, by looking at the ranking of annual mean wind speeds. Picking a region covering eastern England as an example, our analyses show that the wind speeds there over the past ~ 20 yr are within the range expected from natural variability, but do not span the full range of variability of the 140-yr data set. The calendar-year 2010 is however found to have the lowest mean wind speed on record for this region.

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Climate field reconstruction of North Atlantic sea surface temperatures

10.5194/egusphere-egu21-9865 ◽

2021 ◽

Author(s):

Tine Nilsen ◽

Stefanie Talento

Keyword(s):

North Atlantic ◽

Input Data ◽

Signal To Noise Ratio ◽

Sea Surface ◽

Sea Surface Temperatures ◽

Data Sampling ◽

Field Reconstruction ◽

Surface Temperatures ◽

Instrumental Observations ◽

Climate Field Reconstruction

Pseudo-proxy experiments test the skill and sensitivity of two extended climate field reconstruction (CFR) methodologies in reconstructing northern North Atlantic Summer sea surface temperatures (SSTs). The Summer target data originate from one millennium-long simulation of the CESM LME (Otto-Bliesner et al. 2016) .&#160; The experiments test the reconstruction skill systematically for input data mimicking SST marine proxies and instrumental observations, including characteristics such as sparse distribution in space, varying signal-to noise ratio and age uncertainties.The Bayesian hierarchical model BARCAST&#160;assumes implicitly that the target variable is described as an AR(1) process in time (Tingley & Huybers 2010), while the proxy surrogate reconstruction (PSR) method&#160;makes no such assumption (Graham et al. 2007). The PSR selects climate analogues from the simulated instrumental period in our study.Results show that both methodologies generate skillful reconstructions for perfectly dated input data, and the PSR is superior when realistic noise levels are chosen for the input data. When the input is perturbed with age-uncertainties, the methodologies are unable to generate acceptable skillful reconstructions. Facilitating in form of data clustering is tested for both methodologies in the attempt of improving reconstruction skill. This proves successful for the PSR methodology, with the best skill obtained using n=3 clusters over the reconstruction region.Additionally, and addressed as a topic for discussion, we detect weak temporal persistence in the input data and the BARCAST reconstructions. The lack of SST persistence is found to be partly due to the input data sampling frequency: Summer means (June, July, August) averaged for every year. Analyses show that the simulated SST data exhibit weaker memory from one Summer to the next, compared to year-to-year variability based on annual means. Similar results are also found for instrumental observations. This finding stands in contrast to results of previous studies on terrestrial reconstruction, where climate reconstructions and individual proxy records exhibit strong persistence properties, also targeting the Summer season (Werner et al. 2018, Nilsen et al. 2018),.&#160;References:Graham, N.E. et al. (2007), Clim. Change, 83, 241-285, doi: 10.1007/s10584-007-9239-2Otto-Bliesner, B.L. et al (2016), Bull. Amer. Meteor. Soc., 97, 735-754, doi: 10.1175/BAMS-D-14-00233.1&#160;Nilsen, T. et al. (2018), Clim. Past, 14, 947-967, doi: 10.5194/cp-14-947-2018Tingley, M. P. and P. Huybers (2010a), J. Clim., 23, 2759&#8211;2781, doi: 10.1175/2009JCLI3015.1Werner, J. P. et al. (2018), Clim. Past, 14, 527-557, doi: 10.5194/cp-14-527-2018

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Tropospheric ozone based on S5P-BASCOE and extension to the past based on OMI and GOME-2 observation.

10.5194/egusphere-egu21-14237 ◽

2021 ◽

Author(s):

Klaus-Peter Heue ◽

Christophe Lerot ◽

Fabian Romahn ◽

Simon Chabrillat ◽

Yves Christophe ◽

...

Keyword(s):

Chemical Reactions ◽

Tropospheric Ozone ◽

Total Ozone ◽

Reanalysis Data ◽

Data Set ◽

The Past ◽

Stratospheric Intrusion ◽

Non Linear ◽

Linear Effects

Ozone in the troposphere has mainly two sources, the first one is stratospheric intrusion the second one is chemical reactions following the emissions of primary pollutions such as NOx and VOCS.We combine TROPOMI total ozone columns with Microwave Limb Sounding ozone profiles assimilated to BASCOE to retrieve tropospheric ozone columns.Based on a first analysis we observe a decrease of tropospheric ozone during April and May 2020. The lockdown as measure against the Corona pandemic also caused an economic shutdown, and thereby a reduction of primary pollutants mainly NOx. Within the cities centres the lack of NOx caused an increase in tropospheric ozone, due to non linear effects in the ozone NOx chemistry. Outside the cities however a decrease might be expected. Thereby the tropospheric ozone reduction in April May might be caused by the lockdown due to the COVID-19.However the natural variabilty is high caused by metrological conditions. To redcue the influnece of indiviual metrological situation the timeseries is expanded to the past by using additional sensors like GOME-2 and OMI, combined with the BASCOE reanalysis data set BRAM. The tropospheric columns are haromized using the same time and latitude depended bias added as for harmonizing the total columns. Therby we generated a typical anual mean data set, where the exceptional year of 2020 can be compared to.

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