scholarly journals Reconstructing surface temperature changes over the past 600 years using climate model simulations with data assimilation

2010 ◽  
Vol 115 (D9) ◽  
Author(s):  
H. Goosse ◽  
E. Crespin ◽  
A. de Montety ◽  
M. E. Mann ◽  
H. Renssen ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Climate Model ◽  
Temperature Changes ◽  
Model Simulations ◽  
The Past ◽  
Climate Model Simulations

scholarly journals Comparison and assessment of large-scale surface temperature in climate model simulations

2019 ◽  
Vol 5 (1) ◽  
pp. 67-85
Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó
Keyword(s):  
Surface Temperature ◽  
Seasonal Changes ◽  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Temperature Variability ◽  
Data Driven ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.

scholarly journals Teleconnections and relationship between the El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) in reconstructions and models over the past millennium

2020 ◽  
Vol 16 (2) ◽  
pp. 743-756 ◽  
Author(s):  
Christoph Dätwyler ◽  
Martin Grosjean ◽  
Nathan J. Steiger ◽  
Raphael Neukom
Keyword(s):  
Climate Model ◽  
El Nino ◽  
Southern Oscillation ◽  
Southern Annular Mode ◽  
Last Millennium ◽  
Annular Mode ◽  
Model Simulations ◽  
The Past ◽  
Climate Model Simulations ◽  
Past Millennium

Abstract. The climate of the Southern Hemisphere (SH) is strongly influenced by variations in the El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). Because of the limited length of instrumental records in most parts of the SH, very little is known about the relationship between these two key modes of variability over time. Using proxy-based reconstructions and last-millennium climate model simulations, we find that ENSO and SAM indices are mostly negatively correlated over the past millennium. Pseudo-proxy experiments indicate that currently available proxy records are able to reliably capture ENSO–SAM relationships back to at least 1600 CE. Palaeoclimate reconstructions show mostly negative correlations back to about 1400 CE. An ensemble of last-millennium climate model simulations confirms this negative correlation, showing a stable correlation of approximately −0.3. Despite this generally negative relationship we do find intermittent periods of positive ENSO–SAM correlations in individual model simulations and in the palaeoclimate reconstructions. We do not find evidence that these relationship fluctuations are caused by exogenous forcing nor by a consistent climate pattern. However, we do find evidence that strong negative correlations are associated with strong positive (negative) anomalies in the Interdecadal Pacific Oscillation and the Amundsen Sea Low during periods when SAM and ENSO indices are of opposite (equal) sign.

Power-law frequency scaling of surface temperature spatial degrees of freedom – estimated from instrumental data, reanalysis and climate model simulations

2021 ◽  
Author(s):  
Torben Kunz ◽  
Thomas Laepple
Keyword(s):  
Surface Temperature ◽  
Power Law ◽  
Degrees Of Freedom ◽  
Climate Model ◽  
Frequency Scaling ◽  
Frequency Spectra ◽  
Instrumental Data ◽  
Model Simulations ◽  
Climate Model Simulations ◽  
Spatial Degrees Of Freedom

<p>What is the spatial scale of climate fluctuations, and how does this scale depend on the timescale under consideration? To answer this question, the spatio-temporal correlation structure of global surface temperature fields is characterized, for the period 1850-present, by estimating frequency spectra of the effective spatial degrees of freedom (ESDOF). These ESDOF spectra serve as a simple summarizing metric of the frequency-dependent spatial auto-correlation function. ESDOF spectra are estimated from: (a) the HadCRUT global gridded temperature anomaly dataset, based exclusively on instrumental measurements, and including detailed error variance estimates; (b) the NOAA 20th Century Reanalysis; and (c) a large ensemble of CMIP historical climate model simulations. When comparing (i) error corrected ESDOF spectra from the instrumental data to (ii) those obtained from the reanalysis and the model simulations, with HadCRUT data gaps imposed, results are found to be highly consistent among the three data sources. When the analysis is applied to the entire globe, the ESDOF spectra exhibit an almost uniform power-law frequency scaling with about 100 ESDOFs at monthly timescales and only about 2 ESDOFs at multidecadal timescales. Second-order differences in this scaling behaviour are found when the analysis is restricted to various spatial subdomains of the globe, namely, the tropics, extra-tropics, land areas, and ocean areas. A few implications of the diagnosed ESDOF reduction towards the longer timescales are briefly discussed.</p>

scholarly journals Simulating the climate response to atmospheric oxygen variability in the Phanerozoic: a focus on the Holocene, Cretaceous and Permian

2019 ◽  
Vol 15 (4) ◽  
pp. 1463-1483 ◽  
Author(s):  
David C. Wade ◽  
Nathan Luke Abraham ◽  
Alexander Farnsworth ◽  
Paul J. Valdes ◽  
Fran Bragg ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Oxygen Content ◽  
Climate Model ◽  
Atmospheric Oxygen ◽  
Climate Response ◽  
Model Simulations ◽  
Model Version ◽  
Climate Model Simulations ◽  
Hadley Centre ◽  
Global Mean

Abstract. The amount of dioxygen (O2) in the atmosphere may have varied from as little as 5 % to as much as 35 % during the Phanerozoic eon (54 Ma–present). These changes in the amount of O2 are large enough to have led to changes in atmospheric mass, which may alter the radiative budget of the atmosphere, leading to this mechanism being invoked to explain discrepancies between climate model simulations and proxy reconstructions of past climates. Here, we present the first fully 3-D numerical model simulations to investigate the climate impacts of changes in O2 under different climate states using the coupled atmosphere–ocean Hadley Centre Global Environmental Model version 3 (HadGEM3-AO) and Hadley Centre Coupled Model version 3 (HadCM3-BL) models. We show that simulations with an increase in O2 content result in increased global-mean surface air temperature under conditions of a pre-industrial Holocene climate state, in agreement with idealised 1-D and 2-D modelling studies. We demonstrate the mechanism behind the warming is complex and involves a trade-off between a number of factors. Increasing atmospheric O2 leads to a reduction in incident shortwave radiation at the Earth's surface due to Rayleigh scattering, a cooling effect. However, there is a competing warming effect due to an increase in the pressure broadening of greenhouse gas absorption lines and dynamical feedbacks, which alter the meridional heat transport of the ocean, warming polar regions and cooling tropical regions. Case studies from past climates are investigated using HadCM3-BL and show that, in the warmest climate states in the Maastrichtian (72.1–66.0 Ma), increasing oxygen may lead to a temperature decrease, as the equilibrium climate sensitivity is lower. For the Asselian (298.9–295.0 Ma), increasing oxygen content leads to a warmer global-mean surface temperature and reduced carbon storage on land, suggesting that high oxygen content may have been a contributing factor in preventing a “Snowball Earth” during this period of the early Permian. These climate model simulations reconcile the surface temperature response to oxygen content changes across the hierarchy of model complexity and highlight the broad range of Earth system feedbacks that need to be accounted for when considering the climate response to changes in atmospheric oxygen content.

scholarly journals The 15th century Arctic warming in coupled model simulations with data assimilation

2009 ◽  
Vol 5 (3) ◽  
pp. 389-401 ◽  
Author(s):  
E. Crespin ◽  
H. Goosse ◽  
T. Fichefet ◽  
M. E. Mann
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Climate Model ◽  
Three Dimensional ◽  
Coupled Model ◽  
Internal Variability ◽  
The Arctic ◽  
Arctic Warming ◽  
The Past ◽  
Past Millennium

Abstract. An ensemble of simulations of the climate of the past millennium conducted with a three-dimensional climate model of intermediate complexity are constrained to follow temperature histories obtained from a recent compilation of well-calibrated surface temperature proxies using a simple data assimilation technique. Those simulations provide a reconstruction of the climate of the Arctic that is compatible with the model physics, the forcing applied and the proxy records. Available observational data, proxy-based reconstructions and our model results suggest that the Arctic climate is characterized by substantial variations in surface temperature over the past millennium. Though the most recent decades are likely to be the warmest of the past millennium, we find evidence for substantial past warming episodes in the Arctic. In particular, our model reconstructions show a prominent warm event during the period 1470–1520. This warm period is likely related to the internal variability of the climate system, that is the variability present in the absence of any change in external forcing. We examine the roles of competing mechanisms that could potentially produce this anomaly. This study leads us to conclude that changes in atmospheric circulation, through enhanced southwesterly winds towards northern Europe, Siberia and Canada, are likely the main cause of the late 15th/early 16th century Arctic warming.

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