scholarly journals An “Observational Large Ensemble” to Compare Observed and Modeled Temperature Trend Uncertainty due to Internal Variability

2017 ◽  
Vol 30 (19) ◽  
pp. 7585-7598 ◽  
Author(s):  
Karen A. McKinnon ◽  
Andrew Poppick ◽  
Etienne Dunn-Sigouin ◽  
Clara Deser
Keyword(s):  
North America ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Large Ensemble ◽  
Near Surface ◽  
Temperature Trends ◽  
Irreducible Uncertainty ◽  
Statistical Resampling

Abstract Estimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-yr (1966–2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely overestimated in LENS, which has an average amplification of variability of 32% across North America. The amplification of variability is greatest in the western United States and Alaska. The observationally derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an “Observational Large Ensemble” (OLENS). The members of OLENS indicate the range of observationally constrained, spatially consistent temperature trends that could have been observed over the past 50 years if a different sequence of internal variability had unfolded. The smaller trend uncertainty in OLENS suggests that is easier to detect the historical climate change signal in observations than in any given member of LENS.

scholarly journals How large does a large ensemble need to be?

2020 ◽  
Vol 11 (4) ◽  
pp. 885-901
Author(s):  
Sebastian Milinski ◽  
Nicola Maher ◽  
Dirk Olonscheck
Keyword(s):  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Forced Response ◽  
Ensemble Size ◽  
Max Planck ◽  
Large Ensemble ◽  
Acceptable Error ◽  
Near Surface ◽  
Temperature And Precipitation

Abstract. Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool for quantifying the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the examples of global mean near-surface air temperature, local temperature and precipitation, and variability in the El Niño–Southern Oscillation (ENSO) region and central United States for the Max Planck Institute Grand Ensemble (MPI-GE). Estimating the required ensemble size is relevant not only for designing or choosing a large ensemble but also for designing targeted sensitivity experiments with a model. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. We show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity.

scholarly journals The Role of Hadley Circulation and Lapse-Rate Changes for the Future European Summer Climate

2018 ◽  
Vol 32 (2) ◽  
pp. 385-404 ◽  
Author(s):  
Roman Brogli ◽  
Nico Kröner ◽  
Silje Lund Sørland ◽  
Daniel Lüthi ◽  
Christoph Schär
Keyword(s):  
Hadley Circulation ◽  
Global Climate Models ◽  
Hadley Cell ◽  
Lapse Rate ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Minor Importance ◽  
Near Surface ◽  
Summer Warming ◽  
The Mediterranean

Abstract By the end of the century, climate projections for southern Europe exhibit an enhanced near-surface summer warming in response to greenhouse gas emissions, which is known as the Mediterranean amplification. Possible causes for this amplified warming signal include a poleward Hadley cell expansion as well as tropospheric lapse-rate changes. In this work, regional climate model (RCM) simulations driven by three different global climate models (GCMs) are performed, representing the RCP8.5 emission scenario. For every downscaled GCM, the climate change signal over Europe is separated into five contributions by modifying the lateral boundary conditions of the RCM. This simulation strategy is related to the pseudo–global warming method. The results show that a poleward expansion of the Hadley cell is of minor importance for the Mediterranean amplification. During summer, the simulated Hadley circulation is weak, and projections show no distinct expansion in the European sector. The north–south contrast in lapse-rate changes is suggested as the most important factor causing the Mediterranean amplification. Lapse-rate changes are projected throughout Europe, but are weaker over the Mediterranean than over northern Europe (around 0.15 vs 0.3 K km−1 by the end of the century). The weaker lapse-rate changes result in a strong near-surface summer warming over the Mediterranean, since the upper-tropospheric warming is of similar magnitude throughout Europe. The differing lapse-rate changes can be understood as a thermodynamic response to lower-tropospheric humidity contrasts.

scholarly journals Detecting sulphate aerosol geoengineering with different methods

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Y. T. Eunice Lo ◽  
Andrew J. Charlton-Perez ◽  
Fraser C. Lott ◽  
Eleanor J. Highwood
Keyword(s):  
Climate Models ◽  
Surface Air Temperature ◽  
High Sensitivity ◽  
Internal Variability ◽  
Detection Methods ◽  
Sulphate Aerosol ◽  
Near Surface ◽  
Future Scenario ◽  
External Forcings ◽  
Global Mean

Abstract Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. We investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr−1 of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection when no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.

scholarly journals What does global mean temperature tell us about local climate?

2015 ◽  
Vol 373 (2054) ◽  
pp. 20140426 ◽  
Author(s):  
Rowan Sutton ◽  
Emma Suckling ◽  
Ed Hawkins
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Climate Impacts ◽  
Climate Projections ◽  
Local Climate ◽  
Forced Responses ◽  
The Relationship ◽  
Global Mean

The subject of climate feedbacks focuses attention on global mean surface air temperature (GMST) as the key metric of climate change. But what does knowledge of past and future GMST tell us about the climate of specific regions? In the context of the ongoing UNFCCC process, this is an important question for policy-makers as well as for scientists. The answer depends on many factors, including the mechanisms causing changes, the timescale of the changes, and the variables and regions of interest. This paper provides a review and analysis of the relationship between changes in GMST and changes in local climate, first in observational records and then in a range of climate model simulations, which are used to interpret the observations. The focus is on decadal timescales, which are of particular interest in relation to recent and near-future anthropogenic climate change. It is shown that GMST primarily provides information about forced responses, but that understanding and quantifying internal variability is essential to projecting climate and climate impacts on regional-to-local scales. The relationship between local forced responses and GMST is often linear but may be nonlinear, and can be greatly complicated by competition between different forcing factors. Climate projections are limited not only by uncertainties in the signal of climate change but also by uncertainties in the characteristics of real-world internal variability. Finally, it is shown that the relationship between GMST and local climate provides a simple approach to climate change detection, and a useful guide to attribution studies.

scholarly journals Externally Forced and Internal Variability in Ensemble Climate Simulations of the Maunder Minimum

2005 ◽  
Vol 18 (20) ◽  
pp. 4253-4270 ◽  
Author(s):  
Masakazu Yoshimori ◽  
Thomas F. Stocker ◽  
Christoph C. Raible ◽  
Manuel Renold
Keyword(s):  
Time Scales ◽  
Climate Model ◽  
Signal To Noise Ratio ◽  
Surface Air Temperature ◽  
Volcanic Eruptions ◽  
Internal Variability ◽  
Maunder Minimum ◽  
Response Patterns ◽  
Near Surface ◽  
Transient Simulations

Abstract The response of the climate system to natural, external forcing during the Maunder Minimum (ca. a.d. 1645–1715) is investigated using a comprehensive climate model. An ensemble of six transient simulations is produced in order to examine the relative importance of externally forced and internally generated variability. The simulated annual Northern Hemisphere and zonal-mean near-surface air temperature agree well with proxy-based reconstructions on decadal time scales. A mean cooling signal during the Maunder Minimum is masked by the internal unforced variability in some regions such as Alaska, Greenland, and northern Europe. In general, temperature exhibits a better signal-to-noise ratio than precipitation. Mean salinity changes are found in basin averages. The model also shows clear response patterns to volcanic eruptions. In particular, volcanic forcing is projected onto the winter North Atlantic Oscillation index following the eruptions. It is demonstrated that the significant spread of ensemble members is possible even on multidecadal time scales, which has an important implication in coordinating comparisons between model simulations and regional reconstructions.

scholarly journals How Well Do We Know ENSO’s Climate Impacts over North America, and How Do We Evaluate Models Accordingly?

2018 ◽  
Vol 31 (13) ◽  
pp. 4991-5014 ◽  
Author(s):  
Clara Deser ◽  
Isla R. Simpson ◽  
Adam. S. Phillips ◽  
Karen A. McKinnon
Keyword(s):  
North America ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Forced Response ◽  
Climate Impacts ◽  
Model Assessment ◽  
Ensemble Spread ◽  
Tropical Eastern Pacific ◽  
Sampling Variability ◽  
Observational Uncertainty

The role of sampling variability in ENSO composites of winter surface air temperature and precipitation over North America during the period 1920–2013 is assessed for observations and ensembles of coupled model simulations in which sea surface temperature anomalies in the tropical eastern Pacific are nudged to those of the real world. The individual members of each model ensemble show a surprising amount of diversity in their ENSO composites, despite being constructed from the same observed set of 18 El Niño and 14 La Niña events. For a given model, this ensemble spread can only be due to sampling variability, that is, aliasing of internal variability that is unrelated to ENSO, which in turn is shown to arise from internal atmospheric dynamics rather than coupled ocean–atmosphere processes. Analogous ensemble spread is evident in 2000 synthetic ENSO composites based on observations using random sampling techniques. These synthetic composites provide information on the range of spatial patterns and amplitudes associated with imperfect estimation of the forced ENSO signal in the observational record. In some locations, the amplitude of the estimated ENSO signal can vary by more than a factor of two. This observational uncertainty necessitates an approach to model assessment that considers not only the model’s forced response to ENSO, given by its ensemble-mean ENSO composite, but also its representation of internal variability unrelated to ENSO. Such an approach is used to reveal fidelities and shortcomings in the Community Earth System Model, version 1.

The role of internal variability in climate change projections of North American surface air temperature and temperature extremes in CanESM2 large ensemble simulations

2020 ◽  
Author(s):  
Bin Yu ◽  
Guilong Li ◽  
Shangfeng Chen ◽  
Hai Lin
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
North American ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Temperature Extremes ◽  
Large Ensemble ◽  
Climate Simulations ◽  
Ensemble Mean

<p>Recent studies indicated that the internal climate variability plays an important role in various aspects of projected climate changes on regional and local scales. Here we present results of the spreads in projected trends of wintertime North American surface air temperature and extremes indices of warm and cold days over the next half-century, by analyzing a 50-member large ensemble of climate simulations conducted with CanESM2. CanESM2 simulations confirm the important role of internal variability in projected surface temperature trends as demonstrated in previous studies. Yet the spread in North American warming trends in CanESM2 is generally smaller than those obtained from CCSM3 and ECHAM5 large ensemble simulations. Despite this, large spreads in the climate means as well as climate change trends of North American temperature extremes are apparent in CanESM2, especially in the projected cold day trends. The ensemble mean of forced climate simulations reveals high risks of warm days over the western coast and north Canada, as well as a weakening belt of cold days extending from Alaska to the northeast US. The individual ensemble members differ from the ensemble mean mainly in magnitude of the warm day trends, but depart from the ensemble mean in conspicuous ways, including spatial pattern and magnitude, of the cold day trends. The signal-to-noise ratio pattern of the warm day trend resembles that of the surface air temperature trend; with stronger signals over north Canada, Alaska, and the southwestern US than the midsection of the continent. The projected cold day patterns reveal strong signals over the southwestern US, north Canada, and the northeastern US. In addition, the internally generated components of temperature and temperature extreme trends exhibit spatial coherences over North America, and are comparable to the externally forced trends. The large-scale atmospheric circulation-induced temperature variability influences these trends. Overall, our results suggest that climate change trends of North American temperature extremes are likely very uncertain and need to be applied with caution.</p>

scholarly journals Enhanced cold-season warming in semi-arid regions

2012 ◽  
Vol 12 (12) ◽  
pp. 5391-5398 ◽  
Author(s):  
J. Huang ◽  
X. Guan ◽  
F. Ji
Keyword(s):  
North America ◽  
Land Surface ◽  
Temperature Increase ◽  
Arid Regions ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Cold Season ◽  
Arid Areas ◽  
Temperature Trends ◽  
Semi Arid

Abstract. This study examined surface air temperature trends over global land from 1901–2009. It is found that the warming trend was particularly enhanced, in the boreal cold season (November to March) over semi-arid regions (with precipitation of 200–600 mm yr−1) showing a temperature increase of 1.53 °C as compared to the global annual mean temperature increase of 1.13 °C over land. In mid-latitude semi-arid areas of Europe, Asia, and North America, temperatures in the cold season increased by 1.41, 2.42, and 1.5 °C, respectively. The semi-arid regions contribute 44.46% to global annual-mean land-surface temperature trend. The mid-latitude semi-arid regions in the Northern Hemisphere contribute by 27.0% of the total, with the mid-latitude semi-arid areas in Europe, Asia, and North America accounting for 6.29%, 13.81%, and 6.85%, respectively. Such enhanced semi-arid warming (ESAW) imply drier and warmer trend of these regions.

scholarly journals Eurasian Arctic climate over the past millennium as recorded in the Akademii Nauk ice core (Severnaya Zemlya)

2013 ◽  
Vol 9 (3) ◽  
pp. 2401-2422 ◽  
Author(s):  
T. Opel ◽  
D. Fritzsche ◽  
H. Meyer
Keyword(s):  
Ice Core ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Ice Age ◽  
Arctic Climate ◽  
The Arctic ◽  
Absolute Minimum ◽  
Near Surface ◽  
Barents And Kara Seas ◽  
Severnaya Zemlya

Abstract. The chronology of the Akademii Nauk (AN) ice core from Severnaya Zemlya (SZ) has been expanded to the last 1100 yr. Here, we present the easternmost high-resolution ice-core climate-proxy records (δ18O and sodium) from the Arctic that provide new perspectives on past climate fluctuations in the Barents and Kara seas region. Multi-annual AN δ18O data as near-surface air-temperature proxy reveal major temperature changes over the last millennium, including the absolute minimum around 1800 and the exceptional warming to a double-peak maximum in the early 20th century. Neither a pronounced Medieval Climate Anomaly nor a Little Ice Age are detectable in the AN δ18O record. In contrast, there is evidence for several abrupt warming and cooling events such as in the 15th and 16th centuries. These abrupt changes are probably caused by shifts in the atmospheric circulation patterns and accompanied sea-ice feedbacks in the Barents and Kara seas region that highlight the role of the internal variability of the Arctic climate system.

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