scholarly journals Imprints of climate forcings in global gridded temperature data

2016 ◽  
Vol 7 (1) ◽  
pp. 231-249 ◽  
Author(s):  
Jiří Mikšovský ◽  
Eva Holtanová ◽  
Petr Pišoft
Keyword(s):  
Climate Variability ◽  
20Th Century ◽  
Southern Oscillation ◽  
Atlantic Multidecadal Oscillation ◽  
Temperature Data ◽  
Data Sets ◽  
Near Surface ◽  
Anthropogenic Forcing ◽  
Internal Climate Variability ◽  
Climate Forcings

Abstract. Monthly near-surface temperature anomalies from several gridded data sets (GISTEMP, Berkeley Earth, MLOST, HadCRUT4, 20th Century Reanalysis) were investigated and compared with regard to the presence of components attributable to external climate forcings (associated with anthropogenic greenhouse gases, as well as solar and volcanic activity) and to major internal climate variability modes (El Niño/Southern Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation and variability characterized by the Trans-Polar Index). Multiple linear regression was used to separate components related to individual explanatory variables in local monthly temperatures as well as in their global means, over the 1901–2010 period. Strong correlations of temperature and anthropogenic forcing were confirmed for most of the globe, whereas only weaker and mostly statistically insignificant connections to solar activity were indicated. Imprints of volcanic forcing were found to be largely insignificant in the local temperatures, in contrast to the clear volcanic signature in their global averages. Attention was also paid to the manifestations of short-term time shifts in the responses to the forcings, and to differences in the spatial fingerprints detected from individual temperature data sets. It is shown that although the resemblance of the response patterns is usually strong, some regional contrasts appear. Noteworthy differences from the other data sets were found especially for the 20th Century Reanalysis, particularly for the components attributable to anthropogenic forcing over land, but also in the response to volcanism and in some of the teleconnection patterns related to the internal climate variability modes.

scholarly journals Imprints of climate forcings in global gridded temperature data

2015 ◽  
Vol 6 (2) ◽  
pp. 2339-2381 ◽  
Author(s):  
J. Mikšovský ◽  
E. Holtanová ◽  
P. Pišoft
Keyword(s):  
20Th Century ◽  
Southern Oscillation ◽  
Internal Variability ◽  
Atlantic Multidecadal Oscillation ◽  
Near Surface ◽  
Explanatory Variables ◽  
Teleconnection Patterns ◽  
Internal Climate Variability ◽  
Volcanic Forcing ◽  
Climate Forcings

Abstract. Monthly near-surface temperature anomalies from several gridded datasets (GISTEMP, Berkeley Earth, MLOST, HadCRUT4, 20th Century Reanalysis) were investigated and compared with regard to the presence of components attributable to external climate forcings (anthropogenic, solar and volcanic) and to major internal climate variability modes (El Niño/Southern Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation and variability characterized by the Trans-Polar Index). Multiple linear regression was used to separate components related to individual explanatory variables in local monthly temperatures as well as in their global means, over the 1901–2010 period. Strong correlations of temperature and anthropogenic forcing were confirmed for most of the globe, whereas only weaker and mostly statistically insignificant connections to solar activity were indicated. Imprints of volcanic forcing were found to be largely insignificant in the local temperatures, in contrast to the clear volcanic signature in their global averages. An attention was also paid to the manifestations of short-term time shifts in the responses to the forcings, and to differences in the spatial fingerprints detected from individual temperature datasets: it is shown that although the resemblance of the response patterns is usually strong, some regional contrasts appear. Noteworthy differences from the other datasets were found especially for the 20th Century Reanalysis, particularly for the components attributable to anthropogenic and volcanic forcing over land, but also in some of the teleconnection patterns related to the internal variability modes.

scholarly journals Multi-annual modes in the 20th century temperature variability in reanalyses and CMIP5 models

2016 ◽  
Vol 9 (11) ◽  
pp. 4097-4109
Author(s):  
Heikki Järvinen ◽  
Teija Seitola ◽  
Johan Silén ◽  
Jouni Räisänen
Keyword(s):  
Climate Variability ◽  
20Th Century ◽  
Climate Models ◽  
Spectral Power ◽  
Singular Spectrum Analysis ◽  
Reanalysis Data ◽  
Cmip5 Models ◽  
Data Sets ◽  
Near Surface ◽  
Annual Variability

Abstract. A performance expectation is that Earth system models simulate well the climate mean state and the climate variability. To test this expectation, we decompose two 20th century reanalysis data sets and 12 CMIP5 model simulations for the years 1901–2005 of the monthly mean near-surface air temperature using randomised multi-channel singular spectrum analysis (RMSSA). Due to the relatively short time span, we concentrate on the representation of multi-annual variability which the RMSSA method effectively captures as separate and mutually orthogonal spatio-temporal components. This decomposition is a unique way to separate statistically significant quasi-periodic oscillations from one another in high-dimensional data sets.The main results are as follows. First, the total spectra for the two reanalysis data sets are remarkably similar in all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. Apart from the slow components related to multi-decadal periodicities, ENSO oscillations with approximately 3.5- and 5-year periods are the most prominent forms of variability in both reanalyses. In 20CR, these are relatively slightly more pronounced than in ERA-20C. Since about the 1970s, the amplitudes of the 3.5- and 5-year oscillations have increased, presumably due to some combination of forced climate change, intrinsic low-frequency climate variability, or change in global observing network. Second, none of the 12 coupled climate models closely reproduce all aspects of the reanalysis spectra, although some models represent many aspects well. For instance, the GFDL-ESM2M model has two nicely separated ENSO periods although they are relatively too prominent as compared with the reanalyses. There is an extensive Supplement and YouTube videos to illustrate the multi-annual variability of the data sets.

scholarly journals Wind speed stilling and its recovery due to internal climate variability

2021 ◽  
Vol 12 (4) ◽  
pp. 1239-1251
Author(s):  
Jan Wohland ◽  
Doris Folini ◽  
Bryn Pickering
Keyword(s):  
Wind Speed ◽  
Climate Variability ◽  
Internal Variability ◽  
Systematic Investigation ◽  
Max Planck ◽  
Near Surface ◽  
Climate Conditions ◽  
Wind Speeds ◽  
Internal Climate Variability ◽  
Long Run

Abstract. Near-surface winds affect many processes on planet Earth, ranging from fundamental biological mechanisms such as pollination to man-made infrastructure that is designed to resist or harness wind. The observed systematic wind speed decline up to around 2010 (stilling) and its subsequent recovery have therefore attracted much attention. While this sequence of downward and upwards trends and good connections to well-established modes of climate variability suggest that stilling could be a manifestation of multidecadal climate variability, a systematic investigation is currently lacking. Here, we use the Max Planck Institute Grand Ensemble (MPI-GE) to decompose internal variability from forced changes in wind speeds. We report that wind speed changes resembling observed stilling and its recovery are well in line with internal climate variability, both under current and future climate conditions. Moreover, internal climate variability outweighs forced changes in wind speeds on 20-year timescales by 1 order of magnitude, despite the fact that smaller, forced changes become relevant in the long run as they represent alterations of mean states. In this regard, we reveal that land use change plays a pivotal role in explaining MPI-GE ensemble-mean wind changes in the representative concentration pathways 2.6, 4.5, and 8.5. Our results demonstrate that multidecadal wind speed variability is of greater relevance than forced changes over the 21st century, in particular for wind-related infrastructure like wind energy.

scholarly journals West Antarctic ice loss influenced by internal climate variability and anthropogenic forcing

2019 ◽  
Vol 12 (9) ◽  
pp. 718-724 ◽  
Author(s):  
Paul R. Holland ◽  
Thomas J. Bracegirdle ◽  
Pierre Dutrieux ◽  
Adrian Jenkins ◽  
Eric J. Steig
Keyword(s):  
Climate Variability ◽  
Anthropogenic Forcing ◽  
Internal Climate Variability ◽  
West Antarctic

Impacts of low-frequency internal climate variability and greenhouse warming on the El Niño-Southern Oscillation

2020 ◽  
pp. 1-38
Author(s):  
Benjamin Ng ◽  
Wenju Cai ◽  
Tim Cowan ◽  
Daohua Bi
Keyword(s):  
Standard Deviation ◽  
Climate Variability ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Cmip5 Models ◽  
Surface Warming ◽  
Internal Climate Variability ◽  
Cp El Niño ◽  
Eastern Equatorial Pacific

AbstractThe El Niño-Southern Oscillation (ENSO) is the dominant mode of interannual climate fluctuations with wide-ranging socio-economic and environmental impacts. Understanding the eastern Pacific (EP) and central Pacific (CP) El Niño response to a warmer climate is paramount, yet the role of internal climate variability in modulating their response is not clear. Using large ensembles, we find that internal variability generates a spread in the standard deviation and skewness of these two El Niño types that is similar to the spread of 17 Coupled Model Intercomparison Project phase 5 (CMIP5) models that realistically simulate ENSO diversity. Based on 40 Community Earth System Model Large Ensemble (CESM-LE) and 99 Max Planck Institute for Meteorology Grand Ensemble (MPI-GE) members, unforced variability can explain more than 90% of the historical EP and CP El Niño standard deviation and all of the ENSO skewness spread in the 17 CMIP5 models. Both CESM-LE and the selected CMIP5 models show increased EP and CP El Niño variability in a warmer climate, driven by a stronger mean vertical temperature gradient in the upper ocean and faster surface warming of the eastern equatorial Pacific. However, MPI-GE shows no agreement in EP or CP standard deviation change. This is due to weaker sensitivity to the warming signal, such that when the eastern equatorial Pacific surface warming is faster, the change in upper ocean vertical temperature gradient tends to be weaker. This highlights that individual models produce a different ENSO response in a warmer climate, and that considerable uncertainty within the CMIP5 ensemble may be caused by internal climate variability.

Trends and Variability in Airmass Frequencies: Indicators of a Changing Climate

2020 ◽  
Vol 33 (19) ◽  
pp. 8603-8617
Author(s):  
Cameron C. Lee
Keyword(s):  
Southern Oscillation ◽  
Weather Conditions ◽  
Atlantic Multidecadal Oscillation ◽  
Global Scale ◽  
Temperature Record ◽  
Near Surface ◽  
Air Masses ◽  
Global Mean Temperature ◽  
Climate Indicators ◽  
Two Indices

AbstractRecent international efforts at communicating climate change have begun using the notion of a climate indicator—a climate-related metric that can be used to track changes in the Earth system over time. Based upon a recently developed global-scale classification of daily air masses, this research examines the trends and variability in the frequencies of these air masses and then utilizes them to develop two nontraditional climate indicators: a warm/cool index (WCI) and a global extremes index (GEI). Results show that both indices trend significantly upward over the 40-yr period of record, indicating an increase in warm-based air masses (WCI) and extreme air masses (GEI). The two indices also exhibit a moderate (GEI) to strong (WCI) association with the global mean temperature record, multiple near-surface climate variables, and other existing climate indicators over that same time, showing promise as global indicators. Shorter-term variability in these indices also show a linear relationship between the WCI and changes in the Atlantic multidecadal oscillation and a nonlinear relationship between GEI and El Niño–Southern Oscillation. While many published climate indicators are based upon a single variable, and/or are regional in scope, the two indices presented herein are unique in that they are representative of the trends in the multivariate (and extreme, in the case of the GEI) weather conditions that are experienced near Earth’s surface, while also being global in scope.

Separating Internal Variability from the Externally Forced Climate Response

2015 ◽  
Vol 28 (20) ◽  
pp. 8184-8202 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Low Frequency ◽  
Internal Variability ◽  
Atlantic Multidecadal Oscillation ◽  
Ensemble Member ◽  
Internal Climate Variability ◽  
Climate Simulations ◽  
Scaling Methods ◽  
Synthetic Time Series

Abstract Separating low-frequency internal variability of the climate system from the forced signal is essential to better understand anthropogenic climate change as well as internal climate variability. Here both synthetic time series and the historical simulations from phase 5 of CMIP (CMIP5) are used to examine several methods of performing this separation. Linear detrending, as is commonly used in studies of low-frequency climate variability, is found to introduce large biases in both amplitude and phase of the estimated internal variability. Using estimates of the forced signal obtained from ensembles of climate simulations can reduce these biases, particularly when the forced signal is scaled to match the historical time series of each ensemble member. These so-called scaling methods also provide estimates of model sensitivities to different types of external forcing. Applying the methods to observations of the Atlantic multidecadal oscillation leads to different estimates of the phase of this mode of variability in recent decades.

scholarly journals Machine Learning Modeling of Climate Variability Impact on River Runoff

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1177
Author(s):  
Mateusz Norel ◽  
Krzysztof Krawiec ◽  
Zbigniew W. Kundzewicz
Keyword(s):  
Machine Learning ◽  
Climate Variability ◽  
River Runoff ◽  
River Flow ◽  
Southern Oscillation ◽  
Absolute Error ◽  
Atlantic Multidecadal Oscillation ◽  
Test Time ◽  
Training Time ◽  
Primary Determinant

The hypothesis of this study was one of existence of spatially organized links between the time series of river runoff and climate variability indices, describing the oscillations in the atmosphere–ocean system: ENSO (El Niño–Southern Oscillation), PDO (Pacific Decadal Oscillation), AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation). The global river flow reconstructions (ERA-20-CM-R) for 18 study areas on six continents and climate variability indices for the period 1901–2010 were used. The split-sample approach was applied, with the period 1901–2000 used for training and 2001–2010 used for testing. The quality measures used in this paper were mean absolute error, dynamic time warping, and top extreme events error. We demonstrated that a machine learning approach (convolution neural network, CNN) trained on climate variability indices can model the river runoff better than the long-term monthly mean baseline, both in univariate (per-cell) and multivariate (multi-cell, regionalized) settings. We compared the models to the baseline in the form of heatmaps and presented results of ablation experiments (test time ablation, i.e., jackknifing, and training time ablation), which suggested that ENSO is the primary determinant among the considered indices.

scholarly journals Evaluation of Leading Modes of Climate Variability in the CMIP Archives

2020 ◽  
Vol 33 (13) ◽  
pp. 5527-5545 ◽  
Author(s):  
John T. Fasullo ◽  
A. S. Phillips ◽  
C. Deser
Keyword(s):  
Climate Variability ◽  
Southern Oscillation ◽  
Internal Variability ◽  
Intrinsic Variability ◽  
Annular Modes ◽  
Internal Climate Variability ◽  
The North ◽  
The Pacific ◽  
The North Atlantic Oscillation ◽  
Modes Of Climate Variability

AbstractThe adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models’ representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. For ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement.

scholarly journals Twentieth century global glacier mass change: an ensemble-based model reconstruction

2021 ◽  
Vol 15 (7) ◽  
pp. 3135-3157
Author(s):  
Jan-Hendrik Malles ◽  
Ben Marzeion
Keyword(s):  
Mass Balance ◽  
Sea Level Rise ◽  
Sea Level ◽  
20Th Century ◽  
Cross Validation ◽  
Data Sets ◽  
Mass Balances ◽  
Specific Mass ◽  
Near Surface ◽  
The Cross

Abstract. Negative glacier mass balances in most of Earth's glacierized regions contribute roughly one-quarter to currently observed rates of sea-level rise and have likely contributed an even larger fraction during the 20th century. The distant past and future of glaciers' mass balances, and hence their contribution to sea-level rise, can only be estimated using numerical models. Since, independent of complexity, models always rely on some form of parameterizations and a choice of boundary conditions, a need for optimization arises. In this work, a model for computing monthly mass balances of glaciers on the global scale was forced with nine different data sets of near-surface air temperature and precipitation anomalies, as well as with their mean and median, leading to a total of 11 different forcing data sets. The goal is to better constrain the glaciers' 20th century sea-level budget contribution and its uncertainty. Therefore, five global parameters of the model's mass balance equations were varied systematically, within physically plausible ranges, for each forcing data set. We then identified optimal parameter combinations by cross-validating the model results against in situ annual specific mass balance observations, using three criteria: model bias, temporal correlation, and the ratio between the observed and modeled temporal standard deviation of specific mass balances. These criteria were chosen in order not to trade lower error estimates by means of the root mean squared error (RMSE) for an unrealistic interannual variability. We find that the disagreement between the different optimized model setups (i.e., ensemble members) is often larger than the uncertainties obtained via the leave-one-glacier-out cross-validation, particularly in times and places where few or no validation data are available, such as the first half of the 20th century. We show that the reason for this is that in regions where mass balance observations are abundant, the meteorological data are also better constrained, such that the cross-validation procedure only partly captures the uncertainty of the glacier model. For this reason, ensemble spread is introduced as an additional estimate of reconstruction uncertainty, increasing the total uncertainty compared to the model uncertainty merely obtained by the cross-validation. Our ensemble mean estimate indicates a sea-level contribution by global glaciers (outside of the ice sheets; including the Greenland periphery but excluding the Antarctic periphery) for 1901–2018 of 69.2 ± 24.3 mm sea-level equivalent (SLE), or 0.59 ± 0.21 mm SLE yr−1. While our estimates lie within the uncertainty range of most of the previously published global estimates, they agree less with those derived from GRACE data, which only cover the years 2002–2018.

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