scholarly journals Potential causes of 15th century Arctic warming using coupled model simulations with data assimilation

2009 ◽  
Vol 5 (1) ◽  
pp. 1-27 ◽  
Author(s):  
E. Crespin ◽  
H. Goosse ◽  
T. Fichefet ◽  
M. E. Mann
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
The Arctic ◽  
Arctic Warming ◽  
The Past ◽  
15Th Century ◽  
Past Millennium

Abstract. An ensemble of simulations of the climate of the past millennium using 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 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 particularly warm period at the end of the 15th century. This warm event is likely related to the internal variability of the climate system. We examine the roles of competing mechanisms that could potentially produce this anomaly. These examinations lead 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 Arctic warming during the late 15th century.

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.

Arctic warming revealed by multiple CMIP6 models: evaluation of historical simulations and quantification of future projection uncertainties

2021 ◽  
pp. 1-52
Author(s):  
Ziyi Cai ◽  
Qinglong You ◽  
Fangying Wu ◽  
Hans W. Chen ◽  
Deliang Chen ◽  
...  
Keyword(s):  
Barents Sea ◽  
Coupled Model ◽  
Internal Variability ◽  
The Arctic ◽  
Future Research ◽  
Total Uncertainty ◽  
Near Surface ◽  
Arctic Warming ◽  
Poleward Heat Transport ◽  
Warming Rate

AbstractThe Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Norwegian Sea, the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, the multi-model ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice higher than rates in the global/Northern Hemisphere. Model uncertainty is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the 21st century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015-2095. It is found that the largest model uncertainties are consistent with the oceanic regions with cold biases in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that the CMIP6 models’ simulation and projection of the Arctic near-surface temperature still exist large inter-model spread and uncertainties, and there are different behaviors over the ocean and land in the Arctic. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread.

scholarly journals Stable climate and surface mass balance in Svalbard over 1979–2013 despite the Arctic warming

2015 ◽  
Vol 9 (1) ◽  
pp. 83-101 ◽  
Author(s):  
C. Lang ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Mass Balance ◽  
Atmospheric Circulation ◽  
Climate Model ◽  
Coupled Model ◽  
Recent Change ◽  
The Arctic ◽  
Surface Mass Balance ◽  
Near Surface ◽  
Arctic Warming ◽  
Surface Mass

Abstract. With the help of the regional climate model MAR (Modèle Atmosphérique Régional) forced by the ERA-Interim reanalysis (MARERA) and the MIROC5 (Model for Interdisciplinary Research on Climate) global model (MARMIROC5) from the CMIP5 (Coupled Model Intercomparison Project) database, we have modelled the climate and surface mass balance of Svalbard at a 10 km resolution over 1979–2013. The integrated total surface mass balance (SMB) over Svalbard modelled by MARERA is negative (−1.6 Gt yr−1) with a large interannual variability (7.1 Gt) but, unlike over Greenland, there has been no acceleration of the surface melt over the past 35 years because of the recent change in atmospheric circulation bringing northwesterly flows in summer over Svalbard, contrasting the recent observed Arctic warming. However, in 2013, the atmospheric circulation changed to a south–southwesterly flow over Svalbard causing record melt, SMB (−20.4 Gt yr−1) and summer temperature. MIROC5 is significantly colder than ERA-Interim over 1980–2005 but MARMIROC5 is able to improve the near-surface MIROC5 results by simulating not significant SMB differences with MARERA over 1980–2005. On the other hand, MIROC5 does not represent the recent atmospheric circulation shift in summer and induces in MARMIROC5 a significant trend of decreasing SMB (−0.6 Gt yr−2) over 1980–2005.

scholarly journals The Paleoclimate reanalysis project

2015 ◽  
Vol 11 (5) ◽  
pp. 4159-4204 ◽  
Author(s):  
S. A. Browning ◽  
I. D. Goodwin
Keyword(s):  
Data Assimilation ◽  
World View ◽  
Internal Variability ◽  
Temporal Variations ◽  
Global Ocean ◽  
Model Data ◽  
Proxy Data ◽  
The Past ◽  
Spatial Fields ◽  
Past Millennium

Abstract. Recent advances in proxy-model data assimilation have made feasible the development of proxy-based reanalyses. Proxy-based reanalyses aim to make optimum use of both proxy and model data while presenting paleoclimate information in an accessible format – they will undoubtedly play a pivotal role in the future of paleoclimate research. In the Paleoclimate Reanalysis Project (PaleoR) we use "off-line" data assimilation to constrain the CESM1 (CAM5) Last Millennial Ensemble (LME) simulation with a globally distributed multivariate proxy dataset, producing a decadal resolution reanalysis of the past millennium. Discrete time periods are "reconstructed" by using anomalous (±0.5σ) proxy climate signals to select an ensemble of climate state analogues from the LME. Prior to assimilation the LME simulates internal variability that is temporally inconsistent with information from the proxy archive. After assimilation the LME is highly correlated to almost all included proxy data, and dynamical relationships between modelled variables are preserved; thus providing a "real-world" view of climate system evolution during the past millennium. Unlike traditional regression based approaches to paleoclimatology, PaleoR is unaffected by temporal variations in teleconnection patterns. Indices representing major modes of global ocean–atmosphere climate variability can be calculated directly from PaleoR spatial fields. PaleoR derived ENSO, SAM, and NAO indices are consistent with observations and published multiproxy reconstructions. The computational efficiency of "off-line" data assimilation allows easy incorporation and evaluation of new proxy data, and experimentation with different setups and model simulations. PaleoR spatial fields can be viewed online at http://climatefutures.mq.edu.au/research/themes/marine/paleor/.

scholarly journals Rates of global temperature change during the past millennium

2011 ◽  
Vol 7 (4) ◽  
pp. 2341-2354
Author(s):  
C. Shen ◽  
W.-C. Wang ◽  
G. Zeng ◽  
Y. Peng ◽  
Y. Xu
Keyword(s):  
Greenhouse Gases ◽  
Surface Temperature ◽  
Temperature Change ◽  
20Th Century ◽  
Time Scale ◽  
Climate Model ◽  
Global Climate Model ◽  
Surface Temperature Change ◽  
The Past ◽  
Past Millennium

Abstract. We examine the characteristics (amplitude and phase) of the temporal variation in the rates of global-mean surface temperature change during the past millennium. The study was conducted by applying 20-, 30-, and 50-yr sliding windows to the observations of recent century and reconstructions of earlier times. The analysis focuses on the characteristics of the 20th century within the context of the millennium as well as their sensitivity to the low frequency variability of sea surface temperature (SST) and time scales. On 20-yr time scale, comparable rates to that of the 20th century in both amplitude and phase occur in earlier nine centuries. The peak in the amplitude of rates in the 20th century on 30-yr time scale, although is not the largest during the past millennium, but is the most persistent. On 50-yr time scale, the 20th century warming rates are the highest and the most persistent during the past millennium. The results also indicate that although the SST variability does not affect much the amplitude of the rates, but the phases is quite different, thus highlighting the importance of the role of oceans in affecting the rates. We also analyzed the characteristics from global climate model (1000–1999 AD) simulations with different climate (solar, volcanic, and greenhouse gases) forcing. Except for the one driven by the solar forcing, other forcing simulates similar amplitudes as the observed ones. However, only greenhouse gases (GHG) forcing can reproduce the persistent high warming rates of the 20th century.

scholarly journals Simulation of observed climate changes in 1850–2014 with climate model INM-CM5

2018 ◽  
Author(s):  
Evgeny Volodin ◽  
Andrey Gritsun
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Climate Changes ◽  
Climate Model ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Arctic Sea Ice Loss ◽  
Global Mean

Abstract. Climate changes observed in 1850-2014 are modeled and studied on the basis of seven historical runs with the climate model INM-CM5 under the scenario proposed for Coupled Model Intercomparison Project, Phase 6 (CMIP6). In all runs global mean surface temperature rises by 0.8 K at the end of the experiment (2014) in agreement with the observations. Periods of fast warming in 1920–1940 and 1980–2000 as well as its slowdown in 1950–1975 and 2000–2014 are correctly reproduced by the ensemble mean. The notable change here with respect to the CMIP5 results is correct reproduction of the slowdown of global warming in 2000–2014 that we attribute to more accurate description of the Solar constant in CMIP6 protocol. The model is able to reproduce correct behavior of global mean temperature in 1980–2014 despite incorrect phases of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation indices in the majority of experiments. The Arctic sea ice loss in recent decades is reasonably close to the observations just in one model run; the model underestimates Arctic sea ice loss by the factor 2.5. Spatial pattern of model mean surface temperature trend during the last 30 years looks close the one for the ERA Interim reanalysis. Model correctly estimates the magnitude of stratospheric cooling.

scholarly journals The Interpretation and Use of Biases in Decadal Climate Predictions

2014 ◽  
Vol 27 (8) ◽  
pp. 2931-2947 ◽  
Author(s):  
Ed Hawkins ◽  
Buwen Dong ◽  
Jon Robson ◽  
Rowan Sutton ◽  
Doug Smith
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Time Dependent ◽  
Start Time ◽  
Transient Climate Response ◽  
Global Mean Temperature ◽  
Decadal Climate ◽  
Highly Correlated ◽  
Hadley Centre

Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

scholarly journals Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

2017 ◽  
Vol 24 (4) ◽  
pp. 681-694 ◽  
Author(s):  
Yuxin Zhao ◽  
Xiong Deng ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Chang Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Climate Model ◽  
Time Window ◽  
Coupled Model ◽  
Circulation Model ◽  
Scale Interactions ◽  
Climate Signals ◽  
Coupled Data Assimilation ◽  
Climate Analysis

Abstract. Climate signals are the results of interactions of multiple timescale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA) pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW) is usually used to collect measured data for an assimilation cycle to increase observational samples that are sequentially assimilated with their original error scales. Given different timescales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system and twin CDA experiments, we address this issue here. Results show that in each coupled medium, an optimal OTW can provide maximal observational information that best fits the characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulting CDA improves the analysis of climate signals greatly. These simple model results provide a guideline for when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.

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.

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