The challenge of estimating high return levels with short records under large internal variability

2020 ◽  
Author(s):  
Joel Zeder ◽  
Erich M. Fischer
Keyword(s):  
Low Frequency ◽  
Block Size ◽  
Internal Variability ◽  
Statistical Modelling ◽  
Industrial Control ◽  
Temperature And Precipitation ◽  
Return Levels ◽  
Large Ensembles ◽  
Block Sizes ◽  
Observational Record

<p>The scientific understanding of changes in climate extremes is mostly limited to moderate definitions of extreme events occurring every few years, due to a lack of long-term observational daily data sets. In order to estimate return levels beyond observed time periods and event magnitudes, extreme events are typically modelled statistically based on extreme value theory. This is challenging since the short observational record may be affected by low-frequency natural internal variability and limits the block size that can be used.</p><p>Here we test some common assumptions in the statistical modelling of extremes based on indices of climatic extremes (Tx7d, Rx1d, Rx5d) using long pre-industrial control runs and initial-condition large ensembles with thousands of years of model data.</p><p>The tail of a distribution fitted to temperature and precipitation maxima is known to be highly sensitive to the compliance with statistical assumptions and choices such as the block size. Typically, 1-year block maxima are extracted from observational time series due to short record length. It is unclear whether these maxima are already in the domain of true extremes suitable for an extreme value analysis. Furthermore, the observational record is too short to sample low-frequency regional variability and potential transient changes in the mean climate. Standard uncertainty estimates (confidence intervals and hypothesis tests) are generally not accounting for potential biases introduced by a dominant mode of climate variability or violated modelling assumptions.</p><p>Based on a 4700-year pre-industrial control simulation and an 84-member ensemble performed with CESM 1.2.2 model, we systematically extend the statistical modelling of temperature and precipitation extremes to larger block-sizes and longer synthetic observational periods. This analysis reveals a considerable influence of climate variability on tail estimates. Furthermore, the use of too small block sizes can induce substantial random as well as systematic biases. Statistical model complexity and thus uncertainty further increases for extremes retrieved from transient large-ensemble members, as non-stationarity has to be accounted for in the model formulation. Thus, the potential of spatial pooling or conditioning on further climatic variables as proxies for a specific climatic mode to derive more robust tail estimates is also evaluated. Findings based on the CESM ensemble are compared with pre-industrial control runs performed with other models in CMIP6 and other initial-condition large ensembles of the CLIVAR large ensemble working group.</p>

Comparing inter-annual variabilities in three regional single model initial-condition large ensembles (SMILE) over Europe

2020 ◽  
Author(s):  
Fabian von Trentini ◽  
Emma E. Aalbers ◽  
Erich M. Fischer ◽  
Ralf Ludwig
Keyword(s):  
Climate Variability ◽  
Heat Waves ◽  
Internal Variability ◽  
Summer Temperature ◽  
Winter Temperature ◽  
Single Model ◽  
Internal Climate Variability ◽  
Annual Variability ◽  
Temperature And Precipitation ◽  
Large Ensembles

<p>Single model large ensembles are widely used model experiments to estimate internal climate variability (here: inter-annual variability). The underlying assumption is that the internal variability of the chosen model is a good approximation of the observed natural variability. In this study, for the first time over Europe, we test this assumption based on the comparison of three regional climate model large ensembles (16 members of an EC-EARTH-RACMO ensemble, 21 members of a CESM-CCLM ensemble, 50 members of a CanESM-CRCM ensemble) for four European domains (British Isles, France, Mid-Europe, Alps). Simulated inter-annual variability is evaluated against E-OBS and the inter-annual variability and its future change are compared across the ensembles. Analyses comprise seasonal temperature and precipitation, as well as indicators for dry periods and heat waves. Results show a large consistency of all three ensembles with E-OBS data for most indicators and regions, validating the abilities of these ensembles to represent natural variability on the annual scale. EC-EARTH-RACMO shows the highest inter-annual variability for winter temperature and precipitation, whereas CESM-CCLM shows the highest variability for summer temperature and precipitation, as well as for heatwaves and dry periods. Despite these model differences, the sign of the future changes in internal variability is largely the same in all models: for summer temperature, summer precipitation and the number of heat waves, the internal variability increases, while it decreases for winter temperature. While dry periods reveal a tendency to increase in variability, the changes of winter precipitation remain less conclusive. The overall consistency across single model large ensembles and observations strengthens the concept of large ensembles, and underlines their great potential for understanding and quantifying internal climate variability and its role in climate change dynamics.</p>

scholarly journals More accurate quantification of model-to-model agreement in externally forced climatic responses over the coming century

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicola Maher ◽  
Scott B. Power ◽  
Jochem Marotzke
Keyword(s):  
Temporal Variability ◽  
Internal Variability ◽  
Forced Response ◽  
Climate Projections ◽  
Temperature And Precipitation ◽  
Large Ensembles ◽  
Model Ensembles ◽  
Climatic Responses ◽  
Precipitation Changes ◽  
Accurate Quantification

AbstractSeparating how model-to-model differences in the forced response (UMD) and internal variability (UIV) contribute to the uncertainty in climate projections is important, but challenging. Reducing UMD increases confidence in projections, while UIV characterises the range of possible futures that might occur purely by chance. Separating these uncertainties is limited in traditional multi-model ensembles because most models have only a small number of realisations; furthermore, some models are not independent. Here, we use six largely independent single model initial-condition large ensembles to separate the contributions of UMD and UIV in projecting 21st-century changes of temperature, precipitation, and their temporal variability under strong forcing (RCP8.5). We provide a method that produces similar results using traditional multi-model archives. While UMD is larger than UIV for both temperature and precipitation changes, UIV is larger than UMD for the changes in temporal variability of both temperature and precipitation, between 20° and 80° latitude in both hemispheres. Over large regions and for all variables considered here except temporal temperature variability, models agree on the sign of the forced response whereas they disagree widely on the magnitude. Our separation method can readily be extended to other climate variables.

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

2019 ◽  
Author(s):  
Sebastian Milinski ◽  
Nicola Maher ◽  
Dirk Olonscheck
Keyword(s):  
Surface Temperature ◽  
Internal Variability ◽  
Forced Response ◽  
Ensemble Size ◽  
Large Ensemble ◽  
Acceptable Error ◽  
Substantial Impact ◽  
Temperature And Precipitation ◽  
Large Ensembles ◽  
Local Surface Temperature

Abstract. Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify 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 surface temperature, local surface temperature and precipitation and variability in the ENSO region and central America. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. First, we determine how much of an available ensemble size is interpretable without a substantial impact of resampling ensemble members. Then, 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 Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

2020 ◽  
Author(s):  
Flavio Lehner ◽  
Clara Deser ◽  
Nicola Maher ◽  
Jochem Marotzke ◽  
Erich Fischer ◽  
...  
Keyword(s):  
Climate Model ◽  
Internal Variability ◽  
Added Value ◽  
Future Climate Change ◽  
Climate Projections ◽  
Good Representation ◽  
Transient Climate Response ◽  
Temperature And Precipitation ◽  
Large Ensembles ◽  
Robust Separation

<p>Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, we revisit the framework from Hawkins and Sutton (2009) for uncertainty partitioning for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. We also investigate forced changes in variability itself, something that is newly possible with SMILEs. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.</p>

scholarly journals Comparing internal variabilities in three regional single model initial-condition large ensembles (SMILE) over Europe

2019 ◽  
Author(s):  
Fabian von Trentini ◽  
Emma E. Aalbers ◽  
Erich M. Fischer ◽  
Ralf Ludwig
Keyword(s):  
Climate Variability ◽  
Heat Waves ◽  
Internal Variability ◽  
Summer Temperature ◽  
Winter Temperature ◽  
Single Model ◽  
Internal Climate Variability ◽  
Annual Variability ◽  
Temperature And Precipitation ◽  
Large Ensembles

Abstract. Single model large ensembles are widely used model experiments to estimate internal climate variability. The underlying assumption is that the internal variability (here: inter-annual variability) of the chosen model is a good approximation of the observed natural (inter-annual) variability. In this study, we test this assumption based on three regional climate model large ensembles (16 members of an EC-EARTH-RACMO ensemble, 21 members of a CESM-CCLM ensemble, 50 members of a CanESM-CRCM ensemble) for four European domains (British Isles, France, Mid-Europe, Alps). Simulated inter-annual variability is evaluated against E-OBS and the inter-annunal variability and its future change are compared across the ensembles. To the knowledge of the authors, this is the first comparison of regional large ensembles over Europe. Analysis comprises seasonal temperature and precipitation, as well as indicators for dry periods and heat waves. Results show a large consistency of all three ensembles with E-OBS data for most indicators and regions, validating the abilities of these ensembles to represent natural variability on the annual scale. EC-EARTH-RACMO shows the highest inter-annual variability for winter temperature and precipitation, whereas CESM-CCLM shows the highest variability for summer temperature and precipitation, as well as for heatwaves and dry periods. Despite these model differences, the sign of the future changes in internal variability is largely the same in all models: for summer temperature, summer precipitation and the number of heat waves, the internal variability increases, while it decreases for winter temperature. Changes of winter precipitation and dry periods are a bit unclear, with a tendency to increase for dry periods. The overall consistency across single model large ensembles and observations strengthens the concept of large ensembles, and underlines their great potential for understanding and quantifying the role of internal climate variability.

scholarly journals Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

2020 ◽  
Author(s):  
Flavio Lehner ◽  
Clara Deser ◽  
Nicola Maher ◽  
Jochem Marotzke ◽  
Erich Fischer ◽  
...  
Keyword(s):  
Climate Model ◽  
Internal Variability ◽  
Potential Method ◽  
Future Climate Change ◽  
Method Error ◽  
Temperature And Precipitation ◽  
Model Response ◽  
Large Ensembles ◽  
Emissions Scenarios ◽  
Robust Separation

Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the iconic framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method error

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 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 Understanding Drivers of Glacier Length Variability Over the Last Millennium

2020 ◽  
Author(s):  
Alan Huston ◽  
Nicholas Siler ◽  
Gerard H. Roe ◽  
Erin Pettit ◽  
Nathan J. Steiger
Keyword(s):  
Climate Variability ◽  
Volcanic Eruptions ◽  
Internal Variability ◽  
Forced Response ◽  
Last Millennium ◽  
External Forcing ◽  
Climate History ◽  
Temperature And Precipitation ◽  
Glacier Length ◽  
Length Changes

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, therefore, the influence of both sources of climate variability must 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 drives most length changes in mountain glaciers that have a response timescale of 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 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 Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6

2020 ◽  
Vol 11 (2) ◽  
pp. 491-508 ◽  
Author(s):  
Flavio Lehner ◽  
Clara Deser ◽  
Nicola Maher ◽  
Jochem Marotzke ◽  
Erich M. Fischer ◽  
...  
Keyword(s):  
Coupled Model ◽  
Internal Variability ◽  
Potential Method ◽  
Added Value ◽  
Future Climate Change ◽  
Climate Projections ◽  
Good Representation ◽  
Transient Climate Response ◽  
Large Ensembles ◽  
Method Bias

Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple single-model initial-condition large ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method bias < 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method bias (up to 50 %), and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.

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