Millennial climate variability: GCM‐simulation and Greenland ice cores

Blender, Richard; Fraedrich, Klaus; Hunt, B. G.

doi:10.1029/2005gl024919

Cited by 36 publications

(46 citation statements)

References 15 publications

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“…Here, long memory is indicative of internal ocean dynamics, such as warming/cooling episodes (Fraedrich and Blender 2003;Thomas et al 2009). Such measures are used in climate models to understand present day climate variable predictability, including their possible response to global climate change (Blender et al 2006;Rogozhina et al 2011). Figure 1 shows n = 1403 irregularly spaced oxygen isotopic ratios from the Greenland Ice Sheet Project 2 (GISP2) core; the series also features missing observations, indicated on the plot.…”

Section: Long-memory Phenomena In Environmental and Climate Science Tmentioning

confidence: 99%

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A wavelet lifting approach to long-memory estimation

2016

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Reliable estimation of long-range dependence parameters is vital in time series. For example, in environmental and climate science such estimation is often key to understanding climate dynamics, variability and often prediction. The challenge of data collection in such disciplines means that, in practice, the sampling pattern is either irregular or blighted by missing observations. Unfortunately, virtually all existing Hurst parameter estimation methods assume regularly sampled time series and require modification to cope with irregularity or missing data. However, such interventions come at the price of inducing higher estimator bias and variation, often worryingly ignored. This article proposes a new Hurst exponent estimation method which naturally copes with data sampling irregularity. The new method is based on a multiscale lifting transform exploiting its ability to produce wavelet-like coefficients on irregular data and, simultaneously, to effect a necessary powerful decorrelation of those coefficients. Simulations show that our method is accurate and effective, performing well against competitors

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Section: Long-memory Phenomena In Environmental and Climate Science Tmentioning

confidence: 99%

“…This in turn leads to model miscalibration and inaccurate past reconstruction, e.g. greenhouse gases, and overestimation of their long-term effect in coupled ocean-atmosphere climate models (Fraedrich and Blender 2003;Wolff 2005;Blender et al 2006).…”

Section: Aggregation Effectsmentioning

confidence: 99%

A wavelet lifting approach to long-memory estimation

2016

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“…GCM control runs (with fixed boundary conditions, i.e., with fixed atmospheric composition, solar output, and orbital parameters and without volcanism) are found to generate a macroweather regime with H ≈ −0.4 out to the extreme lowfrequency limit of the models (several millennia [ Blender et al ., 2006;Rybski et al ., 2008 ;). Since GCMs are essentially weather models with extra couplings, the name "macro weather" is appropriate.…”

Section: Climate Modeling Prediction and Anthropogenic Effectsmentioning

confidence: 99%

What Is Climate?

Lovejoy

2013

EoS Transactions

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Most people have an intuitive understanding of the weather as referring to the state of the atmosphere at a given time and place and of the climate as a kind of average weather. A popular expression of this dichotomy is “the climate is what you expect, the weather is what you get” (Heinlein [1973, p. 352], although often attributed to Mark Twain). Implicit in this belief is the notion of climate as a kind of constant natural state to which the weather would converge if it were averaged over a long enough period. A corollary is that climate change is a consequence of “climate forcings,” which are external to the natural climate system and which tend to prevent averages from converging to their true values. In this framework, past climate change may be attributed to orbital changes, variations in solar output, volcanic eruptions, etc. For the recent period, anthropogenic forcings can be added.

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“…The time scale of soil moisture memory is generally about half a year with significantly longer times (longer than 1 year) for deep soil (Wu and Dickinson, 2004). Other processes such as those in cold regions involving frozen soil, snow and ice and wind driven and thermohaline ocean circulations have much longer memories (Blender et al, 2006). Evaluating global mean surface temperature anomalies of the Goddard Institute for Space Studies (GISS; Hansen et al, 1996; updated at http://data.giss.nasa.gov/gistemp/) shows that significant memories of the climate system with 95% or higher confidence level can be detected from the autocorrelation function of the surface data with time lags shorter than about 8 years (Note that the estimated autocorrelation function can be found in Schwartz, 2007).…”

Section: Methodsmentioning

confidence: 99%

Estimations of climate sensitivity based on top-of-atmosphere radiation imbalance

et al. 2010

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Abstract. Large climate feedback uncertainties limit the accuracy in predicting the response of the Earth's climate to the increase of CO 2 concentration within the atmosphere. This study explores a potential to reduce uncertainties in climate sensitivity estimations using energy balance analysis, especially top-of-atmosphere (TOA) radiation imbalance. The time-scales studied generally cover from decade to century, that is, middle-range climate sensitivity is considered, which is directly related to the climate issue caused by atmospheric CO 2 change. The significant difference between current analysis and previous energy balance models is that the current study targets at the boundary condition problem instead of solving the initial condition problem. Additionally, climate system memory and deep ocean heat transport are considered. The climate feedbacks are obtained based on the constraints of the TOA radiation imbalance and surface temperature measurements of the present climate. In this study, the TOA imbalance value of 0.85 W/m 2 is used. Note that this imbalance value has large uncertainties. Based on this value, a positive climate feedback with a feedback coefficient ranging from −1.3 to −1.0 W/m 2 /K is found. The range of feedback coefficient is determined by climate system memory. The longer the memory, the stronger the positive feedback. The estimated time constant of the climate is large (70∼120 years) mainly owing to the deep ocean heat transport, implying that the system may be not in an equilibrium state under the external forcing during the industrial era. For the doubled-CO 2 climate (or 3.7 W/m 2 forcing), the estimated global warming would be 3.1 K if the current estimate Correspondence to: B. Lin (bing.lin@nasa.gov) of 0.85 W/m 2 TOA net radiative heating could be confirmed. With accurate long-term measurements of TOA radiation, the analysis method suggested by this study provides a great potential in the estimations of middle-range climate sensitivity.

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Millennial climate variability: GCM‐simulation and Greenland ice cores

Cited by 36 publications

References 15 publications

A wavelet lifting approach to long-memory estimation

A wavelet lifting approach to long-memory estimation

What Is Climate?

Estimations of climate sensitivity based on top-of-atmosphere radiation imbalance

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