Linking marine fog variability in Atlantic Canada to changes in large-scale atmospheric and marine features

2020 ◽  
Author(s):  
Patrick Duplessis ◽  
Minghong Zhang ◽  
William Perrie ◽  
George A Isaac ◽  
Rachel Y W Chang
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Reanalysis Data ◽  
Sea Surface ◽  
Atlantic Canada ◽  
Climate Projections ◽  
The North ◽  
Marine Traffic ◽  
Highly Correlated

<p>Marine and coastal fog forms mainly from the cooling of warm and moist air advected over a colder sea surface. Atlantic Canada is one of the foggiest regions of the world due to the strong temperature contrast between the two oceanic currents in the vicinity. Recurring periods of low visibility notably disrupt off-shore operations and marine traffic, but also land and air transportation. On longer time-scales, marine fog variability also has a significant impact on the global radiative budget. Clouds, including fog, are the greatest source of uncertainty in the current climate projections because of their complex feedback mechanisms. Meteorological records indicate a significant negative trend in the occurrence of foggy conditions over the past six decades at most airports in Atlantic Canada, with large internal variability, including interannual and interdecadal variations. Using the airport observations, reanalysis data and climate model outputs, we investigated the various variabilities on the trend, at interannual and interdecadal scales, and attempted to address what caused these changes in fog frequency. Our results show that the strength and position of the North Atlantic Subtropical High as well as the sea-surface temperature of the cold and warm waters near Atlantic Canada were highly correlated with fog occurrence. We applied the derived fog indices on climate model outputs and projected the fog trends and variability in the different future climate scenarios. The results from this study will be compared with those obtained from other methods and the implications will be discussed.</p>

scholarly journals Probabilistic Concepts in Intermediate-Complexity Climate Models: A Snapshot Attractor Picture

2015 ◽  
Vol 29 (1) ◽  
pp. 259-272 ◽  
Author(s):  
Mátyás Herein ◽  
János Márfy ◽  
Gábor Drótos ◽  
Tamás Tél
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Internal Variability ◽  
Co2 Concentration ◽  
Ensemble Average ◽  
Climate Projections ◽  
Intermediate Complexity ◽  
The North

Abstract A time series resulting from a single initial condition is shown to be insufficient for quantifying the internal variability in a climate model, and thus one is unable to make meaningful climate projections based on it. The authors argue that the natural distribution, obtained from an ensemble of trajectories differing solely in their initial conditions, of the snapshot attractor corresponding to a particular forcing scenario should be determined in order to quantify internal variability and to characterize any instantaneous state of the system in the future. Furthermore, as a simple measure of internal variability of any particular variable of the model, the authors suggest using its instantaneous ensemble standard deviation. These points are illustrated with the intermediate-complexity climate model Planet Simulator forced by a CO2 scenario, with a 40-member ensemble. In particular, the leveling off of the time dependence of any ensemble average is shown to provide a much clearer indication of reaching a steady state than any property of single time series. Shifts in ensemble averages are indicative of climate changes. The dynamical character of such changes is illustrated by hysteresis-like curves obtained by plotting the ensemble average surface temperature versus the CO2 concentration. The internal variability is found to be the most pronounced on small geographical scales. The traditionally used 30-yr temporal averages are shown to be considerably different from the corresponding ensemble averages. Finally, the North Atlantic Oscillation (NAO) index, related to the teleconnection paradigm, is also investigated. It is found that the NAO time series strongly differs in any individual realization from each other and from the ensemble average, and climatic trends can be extracted only from the latter.

Constraints on European temperature and rainfall changes from attributed changes

2020 ◽  
Author(s):  
Gabriele Hegerl ◽  
Andrew Ballinger ◽  
Sabine Undorf
Keyword(s):  
Greenhouse Gas ◽  
Temperature Change ◽  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Climate Projections ◽  
Near Surface ◽  
The North ◽  
Mitigation And Adaptation ◽  
The North Atlantic Oscillation

<p>Quantifying and reducing the uncertainty of climate projections will benefit both mitigation and adaptation decisions. Observed climate change provides evaluation of climate model simulated change, but the contribution by different external forcing factors needs to be reliably separated in order to use observational constraints. We revisit this ASK (for Allen et al., 2000; Stott and Kettleborough, 2002) approach to use attributed responses to greenhouse gas forcing to constrain future predictions.</p><p>We derive constraints on the projected near-surface summer temperature change over Europe as well as over three European subregions. The temperature responses to different external forcings (natural and greenhouse-gas (GHG) or combined anthropogenic) are estimated as the multi-model means of historical simulations from the Coupled Model Intercomparison Project 5 and incoming CMIP6, and the range of factors by which they can be scaled and still be consistent with observations since 1950 (E-OBS) given internal variability is calculated and applied to future RCP8.5 simulations.</p><p>Results show that both the response to GHG-only and to the combined anthropogenic (including aerosols etc.) forcing are detectable in the observed temperature change over Europe, and that the response over the Mediterranean region might be underestimated. Observed precipitation changes over Europe are also detected over some regions, although the confounding effects of the North Atlantic Oscillation need to be considered carefully. The results demonstrate the successful application of the ASK method for constraining projections of regional change over Europe.</p><p> </p>

scholarly journals Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe

2020 ◽  
Vol 11 (3) ◽  
pp. 617-640 ◽  
Author(s):  
Andrea Böhnisch ◽  
Ralf Ludwig ◽  
Martin Leduc
Keyword(s):  
Regional Climate Model ◽  
North Atlantic ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Internal Variability ◽  
Single Model ◽  
Central European ◽  
The North ◽  
The North Atlantic Oscillation

Abstract. Central European weather and climate are closely related to atmospheric mass advection triggered by the North Atlantic Oscillation (NAO), which is a relevant index for quantifying internal climate variability on multi-annual timescales. It remains unclear, however, how large-scale circulation variability affects local climate characteristics when downscaled using a regional climate model. In this study, 50 members of a single-model initial-condition large ensemble (LE) of a nested regional climate model are analyzed for a NAO–climate relationship. The overall goal of the study is to assess whether the range of NAO internal variability is represented consistently between the driving global climate model (GCM; the Canadian Earth System Model version 2 – CanESM2) and the nested regional climate model (RCM; the Canadian Regional Climate Model version 5 – CRCM5). Responses of mean surface air temperature and total precipitation to changes in the NAO index value are examined in a central European domain in both CanESM2-LE and CRCM5-LE via Pearson correlation coefficients and the change per unit index change for historical (1981–2010) and future (2070–2099) winters. Results show that statistically robust NAO patterns are found in the CanESM2-LE under current forcing conditions. NAO flow pattern reproductions in the CanESM2-LE trigger responses in the high-resolution CRCM5-LE that are comparable to reanalysis data. NAO–response relationships weaken in the future period, but their inter-member spread shows no significant change. The results stress the value of single-model ensembles for the evaluation of internal variability by pointing out the large differences of NAO–response relationships among individual members. They also strengthen the validity of the nested ensemble for further impact modeling using RCM data only, since important large-scale teleconnections present in the driving data propagate properly to the fine-scale dynamics in the RCM.

DESIGN AND IMPLEMENTATION OF THE OCEANOGRAPHIC MODELING AND OBSERVATION NETWORK (REMO) FOR PHYSICAL OCEANOGRAPHY STUDIES AND OCEAN FORECASTING

2014 ◽  
Vol 31 (2) ◽  
Author(s):  
Jose Antonio Moreira Lima
Keyword(s):  
Atlantic Ocean ◽  
Large Scale ◽  
North Pacific Ocean ◽  
Integrated Approach ◽  
Sea Surface ◽  
Global Ocean ◽  
Height Anomaly ◽  
The North ◽  
Observation Network ◽  
Ocean Forecasting

This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network, named REMO, for Brazilian regional waters. Ocean forecasting has been an important scientific issue over the last decade due to studies related to climate change as well as applications related to short-range oceanic forecasts. The South Atlantic Ocean has a deficit of oceanographic measurements when compared to other ocean basins such as the North Atlantic Ocean and the North Pacific Ocean. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in-situ data. Fortunately, most ocean forecasting systems heavily rely on the assimilation of surface fields such as sea surface height anomaly (SSHA) or sea surface temperature (SST), acquired by environmental satellites, that can accurately provide information that constrain major surface current systems and their mesoscale activity. An integrated approach is proposed here in which the large scale circulation in the Atlantic Ocean is modeled in a first step, and gradually nested into higher resolution regional models that are able to resolve important processes such as the Brazil Current and associated mesoscale variability, continental shelf waves, local and remote wind forcing, and others. This article presents the overall strategy to develop the models using a network of Brazilian institutions and their related expertise along with international collaboration. This work has some similarity with goals of the international project Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView).

scholarly journals The Interpretation and Use of Biases in Decadal Climate Predictions

2014 ◽  
Vol 27 (8) ◽  
pp. 2931-2947 ◽  
Author(s):  
Ed Hawkins ◽  
Buwen Dong ◽  
Jon Robson ◽  
Rowan Sutton ◽  
Doug Smith
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Time Dependent ◽  
Start Time ◽  
Transient Climate Response ◽  
Global Mean Temperature ◽  
Decadal Climate ◽  
Highly Correlated ◽  
Hadley Centre

Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

scholarly journals A high-resolution model of the 100 ka ice-age cycle

1997 ◽  
Vol 25 ◽  
pp. 58-65 ◽  
Author(s):  
L. Tarasov ◽  
W. R. Peltier
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Relaxation Times ◽  
Ice Age ◽  
Adjustment Process ◽  
Glacial Cycle ◽  
Ice Dynamics ◽  
Last Glacial ◽  
The North ◽  
The Last Glacial

Significant improvements to the representation of climate forcing and mass-balance response in a coupled two-dimensional global energy balance climate model (EBM) and vertically integrated ice-sheet model (ISM) have led to the prediction of an ice-volume chronology for the most recent ice-age cycle of the Northern Hemisphere that is close to that inferred from the geological record. Most significant is that full glacial termination is delivered by the model without the need for new physical ingredients. In addition, a relatively close match is achieved between the Last Glacial Maximum (LGM) model ice topography and that of the recently-described ICE-4G reconstruction. These results suggest that large-scale climate system reorganization is not required to explain the main variations of the North American (NA) ice sheets over the last glacial cycle. Lack of sea-ice and marine-ice dynamics in the model leaves the situation over the Eurasian (EA) sector much more uncertain.The incorporation of a gravitationally self-consistent description of the glacial isostatic adjustment process demonstrates that the NA and EA bedrock responses can be adequately represented by simpler damped-relaxation models with characteristic time-scales of 3–5ka and 5 ka, respectively. These relaxation times agree with those independently inferred on the basis of postglacial relative sea-level histories.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

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 Exploring the uncertainties in the aviation soot–cirrus effect

2021 ◽  
Vol 21 (23) ◽  
pp. 17267-17289
Author(s):  
Mattia Righi ◽  
Johannes Hendricks ◽  
Christof Gerhard Beer
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Internal Variability ◽  
Reanalysis Data ◽  
Ice Nucleation ◽  
Cirrus Clouds ◽  
Model Studies ◽  
Model Dynamics ◽  
Analogous Model ◽  
The Impact

Abstract. A global aerosol–climate model, including a two-moment cloud microphysical scheme and a parametrization for aerosol-induced ice formation in cirrus clouds, is applied in order to quantify the impact of aviation soot on natural cirrus clouds. Several sensitivity experiments are performed to assess the uncertainties in this effect related to (i) the assumptions on the ice nucleation abilities of aviation soot, (ii) the representation of vertical updrafts in the model, and (iii) the use of reanalysis data to relax the model dynamics (the so-called nudging technique). Based on the results of the model simulations, a radiative forcing from the aviation soot–cirrus effect in the range of −35 to 13 mW m−2 is quantified, depending on the assumed critical saturation ratio for ice nucleation and active fraction of aviation soot but with a confidence level below 95 % in several cases. Simple idealized experiments with prescribed vertical velocities further show that the uncertainties on this aspect of the model dynamics are critical for the investigated effect and could potentially add a factor of about 2 of further uncertainty to the model estimates of the resulting radiative forcing. The use of the nudging technique to relax model dynamics is proved essential in order to identify a statistically significant signal from the model internal variability, while simulations performed in free-running mode and with prescribed sea-surface temperatures and sea-ice concentrations are shown to be unable to provide robust estimates of the investigated effect. A comparison with analogous model studies on the aviation soot–cirrus effect show a very large model diversity, with a conspicuous lack of consensus across the various estimates, which points to the need for more in-depth analyses on the roots of such discrepancies.

Increased frequency of Eurasian double jets linked to summer heat extremes in Europe

2021 ◽  
Author(s):  
Efi Rousi ◽  
Kai Kornhuber ◽  
Goratz Beobide Arsuaga ◽  
Fei Luo ◽  
Dim Coumou
Keyword(s):  
Zonal Wind ◽  
Large Scale ◽  
Driving Forces ◽  
Linear Regression Analysis ◽  
Reanalysis Data ◽  
Self Organizing Maps ◽  
Upper Troposphere ◽  
The North ◽  
Heat Extremes ◽  
Different Levels

<p>Persistent summer extremes, such as heatwaves and droughts, can have considerable impacts on nature and societies. There is evidence that weather persistence has increased in Europe over the past decades, in association to changes in atmosphere dynamics, but uncertainties remain and the driving forces are not yet well understood. </p><p>Particularly for Europe, the jet stream may affect surface weather significantly by modulating the North Atlantic storm tracks. Here, we examine the hypothesis that high-latitude warming and decreased westerlies in summer result in more double jets, consisting of two distinct maxima of the zonal wind in the upper troposphere, over the Eurasian sector. Previous work has shown that such double jet states are related to persistent blocking-like circulation in the mid-latitudes. </p><p>We adapt a dynamical perspective of heat extreme trends by looking at large scale circulation and in particular, changes in the zonal mean zonal wind in different levels of the upper troposphere. We define clusters of jet states with the use of Self-Organizing Maps and analyze their characteristics. We find an increase in frequency and persistence of a cluster of double jet states for the period 1979-2019 during July-August (in ERA5 reanalysis data). Those states are linked to increased surface temperature and more frequent heatwaves compared to climatology over western, central, and northern Europe. Significant positive double jet anomalies are found to be dominant in the days preceding and/or coinciding with some of the most intense historical heatwaves in Europe, such as those of 2003 and 2018. A linear regression analysis shows that the increase in frequency and persistence of double jet states may explain part of the strong upward trend in heat extremes over these European regions.</p>

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