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.

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

<p>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.  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.  <br>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°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. </p>

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 Understanding drivers of glacier-length variability over the last millennium

2021 ◽  
Vol 15 (3) ◽  
pp. 1645-1662
Author(s):  
Alan Huston ◽  
Nicholas Siler ◽  
Gerard H. Roe ◽  
Erin Pettit ◽  
Nathan J. Steiger
Keyword(s):  
Climate Variability ◽  
Climate Model ◽  
Volcanic Eruptions ◽  
Internal Variability ◽  
Forced Response ◽  
Last Millennium ◽  
External Forcing ◽  
Climate History ◽  
Temperature And Precipitation ◽  
Glacier Length

Abstract. Changes in glacier length reflect the integrated response to local fluctuations in temperature and precipitation resulting from both external forcing (e.g., volcanic eruptions or anthropogenic CO2) and internal climate variability. In order to interpret the climate history reflected in the glacier moraine record, the influence of both sources of climate variability must therefore be considered. Here we study the last millennium of glacier-length variability across the globe using a simple dynamic glacier model, which we force with temperature and precipitation time series from a 13-member ensemble of simulations from a global climate model. The ensemble allows us to quantify the contributions to glacier-length variability from external forcing (given by the ensemble mean) and internal variability (given by the ensemble spread). Within this framework, we find that internal variability is the predominant source of length fluctuations for glaciers with a shorter response time (less than a few decades). However, for glaciers with longer response timescales (more than a few decades) external forcing has a greater influence than internal variability. We further find that external forcing also dominates when the response of glaciers from widely separated regions is averaged. Single-forcing simulations indicate that, for this climate model, most of the forced response over the last millennium, pre-anthropogenic warming, has been driven by global-scale temperature change associated with volcanic aerosols.

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 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 A pragmatic approach for the downscaling and bias correction of regional climate simulations – evaluation in hydrological modeling

2011 ◽  
Vol 4 (1) ◽  
pp. 45-63 ◽  
Author(s):  
T. Marke ◽  
W. Mauser ◽  
A. Pfeiffer ◽  
G. Zängl
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Model Performance ◽  
Reanalysis Data ◽  
Historical Period ◽  
Climate Simulations ◽  
Meteorological Observations ◽  
Climate Model Simulations

Abstract. The present study investigates a statistical approach for the downscaling of climate simulations focusing on those meteorological parameters most commonly required as input for climate change impact models (temperature, precipitation, air humidity and wind speed), including the option to correct biases in the climate model simulations. The approach is evaluated by the utilization of a hydrometeorological model chain consisting of (i) the regional climate model MM5 (driven by reanalysis data at the boundaries of the model domain), (ii) the downscaling and model interface SCALMET, and (iii) the hydrological model PROMET. The results of four hydrological model runs are compared to discharge recordings at the gauge of the Upper Danube Watershed (Central Europe) for the historical period of 1972–2000 on a daily time basis. The comparison reveals that the presented approaches allow for a more accurate simulation of discharge for the catchment of the Upper Danube Watershed and the considered gauge at the outlet in Achleiten. The correction for subgrid-scale variability is shown to reduce biases in simulated discharge compared to the utilization of bilinear interpolation. Further enhancements in model performance could be achieved by a correction of biases in the RCM data within the downscaling process. Although the presented downscaling approach strongly improves the performance of the hydrological model, deviations from the observed discharge conditions persist that are not found when driving the hydrological model with spatially distributed meteorological observations.

scholarly journals Joint bias correction of temperature and precipitation in climate model simulations

2014 ◽  
Vol 119 (23) ◽  
pp. 13,153-13,162 ◽  
Author(s):  
Chao Li ◽  
Eva Sinha ◽  
Daniel E. Horton ◽  
Noah S. Diffenbaugh ◽  
Anna M. Michalak
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations

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 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.

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