scholarly journals The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity

2018 ◽  
Author(s):  
Andrew E. Dessler ◽  
Thorsten Mauritsen ◽  
Bjorn Stevens
Keyword(s):  
Energy Balance ◽  
Solar Energy ◽  
Climate Sensitivity ◽  
Climate Model ◽  
Internal Variability ◽  
Ensemble Member ◽  
Temperature Record ◽  
Central Hypothesis ◽  
Climate Model Simulations ◽  
Historical Temperature

Abstract. Our climate is constrained by the balance between solar energy absorbed by the Earth and terrestrial energy radiated to space. This energy balance has been widely used to infer equilibrium climate sensitivity (ECS) from observations of 20th-century warming. Such estimates yield lower values than other methods and these have been influential in pushing down the consensus ECS range in recent assessments. Here we test the method using a 100-member ensemble of the MPI-ESM1.1 climate model simulations of the period 1850–2005 with known forcing. We calculate ECS in each ensemble member using energy balance, yielding values ranging from 2.1 to 3.9 K. The spread in the ensemble is related to the central hypothesis in the energy budget framework: that global average surface temperature anomalies are indicative of anomalies in outgoing energy (either of terrestrial origin or reflected solar energy). We find that assumption is not well supported over the historical temperature record in the model ensemble or more recent satellite observations. We find that framing energy balance in terms of 500-hPa tropical temperature better describes the planet's energy balance.

scholarly journals The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity

2018 ◽  
Vol 18 (7) ◽  
pp. 5147-5155 ◽  
Author(s):  
Andrew E. Dessler ◽  
Thorsten Mauritsen ◽  
Bjorn Stevens
Keyword(s):  
Energy Balance ◽  
Solar Energy ◽  
Climate Sensitivity ◽  
Internal Variability ◽  
Ensemble Member ◽  
System Model ◽  
Temperature Record ◽  
Max Planck ◽  
The Earth ◽  
Historical Temperature

Abstract. Our climate is constrained by the balance between solar energy absorbed by the Earth and terrestrial energy radiated to space. This energy balance has been widely used to infer equilibrium climate sensitivity (ECS) from observations of 20th-century warming. Such estimates yield lower values than other methods, and these have been influential in pushing down the consensus ECS range in recent assessments. Here we test the method using a 100-member ensemble of the Max Planck Institute Earth System Model (MPI-ESM1.1) simulations of the period 1850–2005 with known forcing. We calculate ECS in each ensemble member using energy balance, yielding values ranging from 2.1 to 3.9 K. The spread in the ensemble is related to the central assumption in the energy budget framework: that global average surface temperature anomalies are indicative of anomalies in outgoing energy (either of terrestrial origin or reflected solar energy). We find that this assumption is not well supported over the historical temperature record in the model ensemble or more recent satellite observations. We find that framing energy balance in terms of 500 hPa tropical temperature better describes the planet's energy balance.

scholarly journals Anthropogenic forcing and response yield observed positive trend in Earth’s energy imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shiv Priyam Raghuraman ◽  
David Paynter ◽  
V. Ramaswamy
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Solar Spectrum ◽  
Internal Variability ◽  
Satellite Observations ◽  
Positive Trend ◽  
Energy Imbalance ◽  
Model Simulations ◽  
Heat Uptake ◽  
Climate Model Simulations

AbstractThe observed trend in Earth’s energy imbalance (TEEI), a measure of the acceleration of heat uptake by the planet, is a fundamental indicator of perturbations to climate. Satellite observations (2001–2020) reveal a significant positive globally-averaged TEEI of 0.38 ± 0.24 Wm−2decade−1, but the contributing drivers have yet to be understood. Using climate model simulations, we show that it is exceptionally unlikely (<1% probability) that this trend can be explained by internal variability. Instead, TEEI is achieved only upon accounting for the increase in anthropogenic radiative forcing and the associated climate response. TEEI is driven by a large decrease in reflected solar radiation and a small increase in emitted infrared radiation. This is because recent changes in forcing and feedbacks are additive in the solar spectrum, while being nearly offset by each other in the infrared. We conclude that the satellite record provides clear evidence of a human-influenced climate system.

scholarly journals Emerging Anthropogenic Influences on the Southcentral Alaska Temperature and Precipitation Extremes and Related Fires in 2019

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 82
Author(s):  
Uma S. Bhatt ◽  
Rick T. Lader ◽  
John E. Walsh ◽  
Peter A. Bieniek ◽  
Richard Thoman ◽  
...  
Keyword(s):  
Climate Model ◽  
Simulation Analysis ◽  
Historical Context ◽  
Fire Risk ◽  
Internal Variability ◽  
Fire Season ◽  
Temperature And Precipitation ◽  
Southcentral Alaska ◽  
Meteorological Observations ◽  
Climate Model Simulations

The late-season extreme fire activity in Southcentral Alaska during 2019 was highly unusual and consequential. Firefighting operations had to be extended by a month in 2019 due to the extreme conditions of hot summer temperature and prolonged drought. The ongoing fires created poor air quality in the region containing most of Alaska’s population, leading to substantial impacts to public health. Suppression costs totaled over $70 million for Southcentral Alaska. This study’s main goals are to place the 2019 season into historical context, provide an attribution analysis, and assess future changes in wildfire risk in the region. The primary tools are meteorological observations and climate model simulations from the NCAR CESM Large Ensemble (LENS). The 2019 fire season in Southcentral Alaska included the hottest and driest June–August season over the 1979–2019 period. The LENS simulation analysis suggests that the anthropogenic signal of increased fire risk had not yet emerged in 2019 because of the CESM’s internal variability, but that the anthropogenic signal will emerge by the 2040–2080 period. The effect of warming temperatures dominates the effect of enhanced precipitation in the trend towards increased fire risk.

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.

Seasonal predictions as a high-resolution large ensemble to study extreme events over recent decades

2021 ◽  
Author(s):  
Timo Kelder ◽  
Louise Slater ◽  
Tim Marjoribanks ◽  
Rob Wilby ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Extreme Events ◽  
Climate Model ◽  
Risk Estimation ◽  
Seasonal Prediction ◽  
Horizontal Resolution ◽  
Ensemble Member ◽  
Model Stability ◽  
Model Fidelity ◽  
Large Ensembles ◽  
Climate Model Simulations

&lt;p&gt;Large ensembles of climate model simulations may be used to assess the likelihood of extreme events, which only have a limited chance of occurring in observed records. In this talk, we discuss how the ECMWF seasonal prediction system SEAS5 can be used to generate a 100-member ensemble over 1981-present. SEAS5 is a global coupled ocean, sea-ice, atmosphere model with a horizontal resolution of 36 km. We introduce an open and reproducible workflow to retrieve Copernicus SEAS5 data and evaluate the ensemble member independence, model stability, and model fidelity. We illustrate how the increased sample size may help risk estimation, detecting trends in 100-year extremes as well as analysing drivers of extreme events that are difficult to discern from limited observational records.&lt;/p&gt;

scholarly journals On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

2018 ◽  
Vol 31 (14) ◽  
pp. 5681-5693 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Jules B. Kajtar ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Regional Differences ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Single Model ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Ensemble Mean ◽  
Climate Model Simulations ◽  
The Impact

Abstract In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

Quantifying uncertainty in projections of future European climate: a multi-model multi-method approach

2020 ◽  
Author(s):  
Lukas Brunner ◽  
Carol McSweeney ◽  
Daniel Befort ◽  
Chris O'Reilly ◽  
Ben Booth ◽  
...  
Keyword(s):  
Climate Model ◽  
Probability Distributions ◽  
Internal Variability ◽  
Precipitation Change ◽  
Future Climate Change ◽  
Climate Projections ◽  
Temperature And Precipitation ◽  
European Climate ◽  
Climate Model Simulations ◽  
Quantifying Uncertainty

&lt;p&gt;Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. Different approaches to constrain, filter, or weight climate model simulations into probabilistic projections have been proposed to provide such estimates. Here six methods are applied to European climate projections using a consistent framework in order to allow a quantitative comparison. &amp;#160;Focus is given to summer temperature and precipitation change in three different spatial regimes in Europe in the period 2041-2060 relative to 1995-2014. The analysis draws on projections from several large initial condition ensembles, the CMIP5 multi-model ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. &amp;#160;&lt;br&gt;The methods included are diverse in their approach to quantifying uncertainty, and include those which apply weighting schemes based on baseline performance and inter-model relationships, so-called ASK (Allen, Stott and Kettleborough) techniques which use optimal fingerprinting to scale the scale the response to external forcings, to those found in observations and Bayesian approaches to estimating probability distributions. Some of the key differences between methods are the uncertainties covered, the treatment of internal variability, and variables and regions used to inform the methods. In spite of these considerable methodological differences, the median projection from the multi-model methods agree on a statistically significant increase in temperature by mid-century by about 2.5&amp;#176;C in the European average. The estimates of spread, in contrast, differ substantially between methods. Part of this large difference in the spread reflects the fact that different methods attempt to capture different sources of uncertainty, and some are more comprehensive in this respect than others. This study, therefore, highlights the importance of providing clear context about how different methods affect the distribution of projections, particularly the in the upper and lower percentiles that are of interest to 'risk averse' stakeholders. Methods find less agreement in precipitation change with most methods indicating a slight increase in northern Europe and a drying in the central and Mediterranean regions, but with considerably different amplitudes. Further work is needed to understand how the underlying differences between methods lead to such diverse results for precipitation.&amp;#160;&lt;/p&gt;

scholarly journals Trends in Downward Solar Radiation at the Surface over North America from Climate Model Projections and Implications for Solar Energy

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Gerardo Andres Saenz ◽  
Huei-Ping Huang
Keyword(s):  
United States ◽  
North America ◽  
Solar Radiation ◽  
Solar Energy ◽  
Climate Model ◽  
Global Climate Model ◽  
The United States ◽  
Model Simulations ◽  
Climate Model Simulations ◽  
The Impact

The projected changes in the downward solar radiation at the surface over North America for late 21st century are deduced from global climate model simulations with greenhouse-gas (GHG) forcing. A robust trend is found in winter over the United States, which exhibits a simple pattern of a decrease of sunlight over Northern USA. and an increase of sunlight over Southern USA. This structure was identified in both the seasonal mean and the mean climatology at different times of the day. It is broadly consistent with the known poleward shift of storm tracks in winter in climate model simulations with GHG forcing. The centennial trend of the downward shortwave radiation at the surface in Northern USA. is on the order of 10% of the climatological value for the January monthly mean, and slightly over 10% at the time when it is midday in the United States. This indicates a nonnegligible influence of the GHG forcing on solar energy in the long term. Nevertheless, when dividing the 10% by a century, in the near term, the impact of the GHG forcing is relatively minor such that the estimate of solar power potential using present-day climatology will remain useful in the coming decades.

scholarly journals Teleconnections and relationship between ENSO and SAM in reconstructions and models over the past millennium

2019 ◽  
Author(s):  
Christoph Dätwyler ◽  
Martin Grosjean ◽  
Nathan J. Steiger ◽  
Raphael Neukom
Keyword(s):  
Climate Model ◽  
Southern Oscillation ◽  
Negative Temperature ◽  
Internal Variability ◽  
Southern Annular Mode ◽  
Teleconnection Patterns ◽  
Modes Of Variability ◽  
Proxy Records ◽  
Climate Model Simulations ◽  
Instrumental Records

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). Due to the temporally very limited instrumental records in most parts of the SH, very little is known about the relationship between these two key modes of variability and its stability over time. Here, we use proxy-based reconstructions and climate model simulations to quantify changes in tropical-extratropical SH teleconnections as represented by the correlation between the ENSO and SAM indices. Reconstructions indicate mostly negative correlations back to around 1400 CE confirming the pattern seen in the instrumental record over the last few decades. An ensemble of last millennium simulations of the model CESM1 confirms this pattern with very stable ensemble mean correlations around −0.3. Individual forced simulations, the pre-industrial control run and the proxy-based reconstructions indicate intermittent periods of positive correlations and particularly strong negative correlations. The fluctuations of the ENSO-SAM correlations are not significantly related to solar nor volcanic forcing in both proxy and model data, indicating that they are driven by internal variability in the climate system. Pseudoproxy experiments indicate that the currently available proxy records are able to reproduce the tropical-extratropical teleconnection patterns back to around 1600 CE. We analyse the spatial temperature and sea level pressure patterns during periods of positive and particularly strong negative teleconnections in the CESM model. Results indicate no consistent pattern during periods where the ENSO-SAM teleconnection changes its sign. However, periods of very strong negative SH teleconnections are associated with negative temperature anomalies across large fractions of the extra-tropical Pacific and a strengthening of the Aleutian Low.

scholarly journals Estimating trends in the global mean temperature record

2017 ◽  
Vol 3 (1) ◽  
pp. 33-53 ◽  
Author(s):  
Andrew Poppick ◽  
Elisabeth J. Moyer ◽  
Michael L. Stein
Keyword(s):  
Radiative Forcing ◽  
Temporal Correlation ◽  
Internal Variability ◽  
Historical Record ◽  
Temperature Record ◽  
Empirical Methods ◽  
Global Mean Temperature ◽  
Data Record ◽  
Historical Temperature ◽  
Global Mean

Abstract. Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to de-emphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate, and time series methods that account for internal variability in nonparametric rather than parametric ways. However, given a limited data record and the presence of internal variability, estimating radiatively forced temperature trends in the historical record necessarily requires some assumptions. Ostensibly empirical methods can also involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to be applicable, but long enough for long-timescale internal variability to be accounted for. In the context of global mean temperatures, empirical methods that appear to de-emphasize assumptions can therefore produce misleading inferences, because the trend over the twentieth century is complex and the scale of temporal correlation is long relative to the length of the data record. We illustrate here how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can allow for a wider array of questions to be addressed. In particular, the model allows one to distinguish, within a single statistical framework, between uncertainties in the shorter-term vs. longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric statistical methods, if attuned to the important characteristics of internal variability, can result in more accurate uncertainty statements about trends.

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