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.

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 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 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 Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

2021 ◽  
Vol 12 (2) ◽  
pp. 401-418
Author(s):  
Nicola Maher ◽  
Sebastian Milinski ◽  
Ralf Ludwig
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Internal Variability ◽  
Climate System ◽  
Single Type ◽  
Special Issue ◽  
Large Ensemble ◽  
Model Simulations ◽  
Different Types ◽  
Climate Model Simulations

Abstract. Single model initial-condition large ensembles (SMILEs) are valuable tools that can be used to investigate the climate system. SMILEs allow scientists to quantify and separate the internal variability of the climate system and its response to external forcing, with different types of SMILEs appropriate to answer different scientific questions. In this editorial we first provide an introduction to SMILEs and an overview of the studies in the special issue “Large Ensemble Climate Model Simulations: Exploring Natural Variability, Change Signals and Impacts”. These studies analyse a range of different types of SMILEs including global climate models (GCMs), regionally downscaled climate models (RCMs), a hydrological model with input from a RCM SMILE, a SMILE with prescribed sea surface temperature (SST) built for event attribution, a SMILE that assimilates observed data, and an initialised regional model. These studies provide novel methods, that can be used with SMILEs. The methods published in this issue include a snapshot empirical orthogonal function analysis used to investigate El Niño–Southern Oscillation teleconnections; the partitioning of future uncertainty into model differences, internal variability, and scenario choices; a weighting scheme for multi-model ensembles that can incorporate SMILEs; and a method to identify the required ensemble size for any given problem. Studies in this special issue also focus on RCM SMILEs, with projections of the North Atlantic Oscillation and its regional impacts assessed over Europe, and an RCM SMILE intercomparison. Finally a subset of studies investigate projected impacts of global warming, with increased water flows projected for future hydrometeorological events in southern Ontario; precipitation projections over central Europe are investigated and found to be inconsistent across models in the Alps, with a continuation of past tendencies in Mid-Europe; and equatorial Asia is found to have an increase in the probability of large fire and drought events under higher levels of warming. These studies demonstrate the utility of different types of SMILEs. In the second part of this editorial we provide a perspective on how three types of SMILEs could be combined to exploit the advantages of each. To do so we use a GCM SMILE and an RCM SMILE with all forcings, as well as a naturally forced GCM SMILE (nat-GCM) over the European domain. We utilise one of the key advantages of SMILEs, precisely separating the forced response and internal variability within an individual model to investigate a variety of simple questions. Broadly we show that the GCM can be used to investigate broad-scale patterns and can be directly compared to the nat-GCM to attribute forced changes to either anthropogenic emissions or volcanoes. The RCM provides high-resolution spatial information of both the forced change and the internal variability around this change at different warming levels. By combining all three ensembles we can gain information that would not be available using a single type of SMILE alone, providing a perspective on future research that could be undertaken using these tools.

scholarly journals Return levels of sub-daily extreme precipitation over Europe

2020 ◽  
Author(s):  
Benjamin Poschlod ◽  
Ralf Ludwig ◽  
Jana Sillmann
Keyword(s):  
Observational Data ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Internal Variability ◽  
Return Level ◽  
Data Set ◽  
Model Simulations ◽  
Return Levels ◽  
Climate Model Simulations ◽  
Precipitation Events

Abstract. Information on the frequency and intensity of extreme precipitation is required by public authorities, civil security departments and engineers for the design of buildings and the dimensioning of water management and drainage schemes. Especially for sub-daily resolution, at which many extreme precipitation events occur, the observational data are sparse in space and time, distributed heterogeneously over Europe and often not publicly available. We therefore consider it necessary to provide an impact-orientated data set of 10-year rainfall return levels over Europe based on climate model simulations and evaluate its quality. Hence, to standardize procedures and provide comparable results, we apply a high-resolution single-model large ensemble (SMILE) of the Canadian Regional Climate Model version 5 (CRCM5) with 50 members in order to assess the frequency of heavy precipitation events over Europe between 1980 and 2009. The application of a SMILE enables a robust estimation of extreme rainfall return levels with the 50 members of 30-year climate simulations providing 1500 years of rainfall data. As the 50 members only differ due to the internal variability of the climate system, the impact of internal variability on the return level values can be quantified. We present 10-year rainfall return levels of hourly to 24-hourly duration with a spatial resolution of 0.11° (12.5 km), which are compared to a large data set of observation-based rainfall return levels of 16 European countries. This observation-based data set was newly compiled and homogenized for this study from 32 different sources. The rainfall return levels of the CRCM5 are able to reproduce the general spatial pattern of extreme precipitation for all sub-daily durations with centred Pearson product-moment coefficients of linear correlation > 0.7 for the area covered with observations. Also, the rainfall intensity of the observational data set is in the range of the climate model generated intensities in 52 % (77 %, 79 %, 84 %, 78 %) of the area for hourly (3-hourly, 6-hourly, 12-hourly, 24-hourly) durations. This results in biases between −19.3 % (hourly) to +8.0 % (24-hourly) averaged over the study area. The range, which is introduced by the application of 50 members, shows a spread of −15 % to +18 % around the median. We conclude that our data set shows good agreement with the observations for 3-hourly to 24-hourly durations in large parts of the study area. Though, for hourly duration and topographically complex regions such as the Alps and Norway, we argue that higher-resolution climate model simulations are needed to improve the results. The 10-year return level data are publicly available (Poschlod, 2020; https://doi.org/10.5281/zenodo.3878887).

scholarly journals The impact of volcanic aerosol on the Northern Hemisphere stratospheric polar vortex: mechanisms and sensitivity to forcing structure

2014 ◽  
Vol 14 (11) ◽  
pp. 16777-16819
Author(s):  
M. Toohey ◽  
K. Krüger ◽  
M. Bittner ◽  
C. Timmreck ◽  
H. Schmidt
Keyword(s):  
High Latitude ◽  
Climate Model ◽  
Polar Vortex ◽  
Volcanic Aerosol ◽  
Stratospheric Polar Vortex ◽  
Model Simulations ◽  
Aerosol Forcing ◽  
Climate Model Simulations ◽  
The Impact ◽  
Forcing Set

Abstract. Observations and simple theoretical arguments suggest that the Northern Hemisphere (NH) stratospheric polar vortex is stronger in winters following major volcanic eruptions. However, recent studies show that climate models forced by prescribed volcanic aerosol fields fail to reproduce this effect. We investigate the impact of volcanic aerosol forcing on stratospheric dynamics, including the strength of the NH polar vortex, in ensemble simulations with the Max Planck Institute Earth System Model. The model is forced by four different prescribed forcing sets representing the radiative properties of stratospheric aerosol following the 1991 eruption of Mt. Pinatubo: two forcing sets are based on observations, and are commonly used in climate model simulations, and two forcing sets are constructed based on coupled aerosol–climate model simulations. For all forcings, we find that temperature and zonal wind anomalies in the NH high latitudes are not directly impacted by anomalous volcanic aerosol heating. Instead, high latitude effects result from robust enhancements in stratospheric residual circulation, which in turn result, at least in part, from enhanced stratospheric wave activity. High latitude effects are therefore much less robust than would be expected if they were the direct result of aerosol heating. While there is significant ensemble variability in the high latitude response to each aerosol forcing set, the mean response is sensitive to the forcing set used. Significant differences, for example, are found in the NH polar stratosphere temperature and zonal wind response to two different forcing data sets constructed from different versions of SAGE II aerosol observations. Significant strengthening of the polar vortex, in rough agreement with the expected response, is achieved only using aerosol forcing extracted from prior coupled aerosol–climate model simulations. Differences in the dynamical response to the different forcing sets used imply that reproducing the polar vortex responses to past eruptions, or predicting the response to future eruptions, depends on accurate representation of the space-time structure of the volcanic aerosol forcing.

scholarly journals Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Hyung-Il Eum ◽  
Philippe Gachon ◽  
René Laprise
Keyword(s):  
Climate Change ◽  
Spatial Variability ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Model Bias ◽  
Model Simulations ◽  
Seasonal Climate ◽  
Ensemble Mean ◽  
Climate Model Simulations

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. These results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.

scholarly journals A Statistical Downscaling Model for Southern Australia Winter Rainfall

2009 ◽  
Vol 22 (5) ◽  
pp. 1142-1158 ◽  
Author(s):  
Yun Li ◽  
Ian Smith
Keyword(s):  
Climate Model ◽  
Close Association ◽  
Large Fraction ◽  
Statistical Downscaling Model ◽  
Winter Rainfall ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Rainfall Anomalies ◽  
Climate Model Simulations ◽  
Emissions Scenario

Abstract A technique for obtaining downscaled rainfall projections from climate model simulations is described. This technique makes use of the close association between mean sea level pressure (MSLP) patterns and rainfall over southern Australia during winter. Principal components of seasonal mean MSLP anomalies are linked to observed rainfall anomalies at regional, gridpoint, and point scales. A maximum of four components is sufficient to capture a relatively large fraction of the observed variance in rainfall at most locations. These are used to interpret the MSLP patterns from a single climate model, which has been used to simulate both present-day and future climate. The resulting downscaled values provide 1) a closer representation of the observed present-day rainfall than the raw climate model values and 2) alternative estimates of future changes to rainfall that arise owing to changes in mean MSLP. While decreases are simulated for later this century (under a single emissions scenario), the downscaled values, in percentage terms, tend to be less.

Sign in / Sign up

Export Citation Format

Share Document