Articles | Volume 6, issue 1
https://doi.org/10.5194/ascmo-6-31-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-6-31-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 Apr 2020
| 30 Apr 2020
Possible impacts of climate change on fog in the Arctic and subpolar North Atlantic
Richard E. Danielson
CORRESPONDING AUTHOR
Danielson Associates Office Inc., Halifax, Nova Scotia, Canada
Fisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada
Minghong Zhang
Fisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada
William A. Perrie
Fisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada
Related authors
No articles found.
Adhi Susilo, Will Perrie, and Bash Toulany
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-256, https://doi.org/10.5194/gmd-2017-256, 2017
Preprint retracted
Short summary
Short summary
Solving nonlinear wave-wave interactions with Boltzmann integral requires solving the domain of the integration correctly. While we are working on finding the loci of integration, we have an idea to find the loci with different way, an explicit way. The new method shows better results than the previous one and the algorithm is easy to follow.
Related subject area
Climate research
Comparing climate time series – Part 4: Annual cycles
Statistical reconstruction of European winter snowfall in reanalysis and climate models based on air temperature and total precipitation
A multi-method framework for global real-time climate attribution
Analysis of the evolution of parametric drivers of high-end sea-level hazards
Comparing climate time series – Part 3: Discriminant analysis
Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone
Spatial heterogeneity of 2015–2017 drought intensity in South Africa's winter rainfall zone
A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
A protocol for probabilistic extreme event attribution analyses
The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany
Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models
Comparison and assessment of large-scale surface temperature in climate model simulations
Future climate emulations using quantile regressions on large ensembles
Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures
Estimates of climate system properties incorporating recent climate change
The joint influence of break and noise variance on the break detection capability in time series homogenization
A space–time statistical climate model for hurricane intensification in the North Atlantic basin
Building a traceable climate model hierarchy with multi-level emulators
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 187–203, https://doi.org/10.5194/ascmo-8-187-2022, https://doi.org/10.5194/ascmo-8-187-2022, 2022
Short summary
Short summary
Most climate time series contain annual and diurnal cycles. However, an objective criterion for deciding whether two time series have statistically equivalent annual and diurnal cycles is lacking, particularly if the residual variability is serially correlated. Such a criterion would be helpful in deciding whether a new version of a climate model better simulates such cycles. This paper derives an objective rule for such decisions based on a rigorous statistical framework.
Flavio Maria Emanuele Pons and Davide Faranda
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 155–186, https://doi.org/10.5194/ascmo-8-155-2022, https://doi.org/10.5194/ascmo-8-155-2022, 2022
Short summary
Short summary
The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
Daniel M. Gilford, Andrew Pershing, Benjamin H. Strauss, Karsten Haustein, and Friederike E. L. Otto
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 135–154, https://doi.org/10.5194/ascmo-8-135-2022, https://doi.org/10.5194/ascmo-8-135-2022, 2022
Short summary
Short summary
We developed a framework to produce global real-time estimates of how human-caused climate change affects the likelihood of daily weather events. A multi-method approach provides ensemble attribution estimates accompanied by confidence intervals, creating new opportunities for climate change communication. Methodological efficiency permits daily analysis using forecasts or observations. Applications with daily maximum temperature highlight the framework's capacity on daily and global scales.
Alana Hough and Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 117–134, https://doi.org/10.5194/ascmo-8-117-2022, https://doi.org/10.5194/ascmo-8-117-2022, 2022
Short summary
Short summary
We use machine learning to assess how different geophysical uncertainties relate to the severity of future sea-level rise. We show how the contributions to coastal hazard from different sea-level processes evolve over time and find that near-term sea-level hazards are driven by thermal expansion and the melting of glaciers and ice caps, while long-term hazards are driven by ice loss from the major ice sheets.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, https://doi.org/10.5194/ascmo-8-97-2022, https://doi.org/10.5194/ascmo-8-97-2022, 2022
Short summary
Short summary
A common problem in climate studies is to decide whether a climate model is realistic. Such decisions are not straightforward because the time series are serially correlated and multivariate. Part II derived a test for deciding wether a simulation is statistically distinguishable from observations. However, the test itself provides no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between stochastic processes.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 31–62, https://doi.org/10.5194/ascmo-8-31-2022, https://doi.org/10.5194/ascmo-8-31-2022, 2022
Short summary
Short summary
Cape Town is situated in a small but ecologically and climatically highly diverse and vulnerable pocket of South Africa uniquely receiving its rain mostly in winter. We show complex structures in the spatial patterns of rainfall seasonality and year-to-year changes in rainfall within this domain, tied to spatial differences in the rain-bearing winds. This allows us to develop a new spatial subdivision of the region to help future studies distinguish spatially distinct climate change responses.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 63–81, https://doi.org/10.5194/ascmo-8-63-2022, https://doi.org/10.5194/ascmo-8-63-2022, 2022
Short summary
Short summary
The
Day Zerowater crisis affecting Cape Town after the severe 2015–2017 drought motivated renewed research interest into causes and projections of rainfall variability and change in this water-stressed region. Unusually few wet months and very wet days characterised the Day Zero Drought. Its extent expanded as it shifted gradually north-eastward, concurrent with changes in the weather systems driving drought. Our results emphasise the need to consider the interplay between drought drivers.
Erica L. Ashe, Nicole S. Khan, Lauren T. Toth, Andrea Dutton, and Robert E. Kopp
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 1–29, https://doi.org/10.5194/ascmo-8-1-2022, https://doi.org/10.5194/ascmo-8-1-2022, 2022
Short summary
Short summary
We develop a new technique to integrate realistic uncertainties in probabilistic models of past sea-level change. The new framework performs better than past methods (in precision, accuracy, bias, and model fit) because it enables the incorporation of previously unused data and exploits correlations in the data. This method has the potential to assess the validity of past estimates of extreme sea-level rise and highstands providing better context in which to place current sea-level change.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
Short summary
Short summary
Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
Short summary
Short summary
Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139, https://doi.org/10.5194/ascmo-6-115-2020, https://doi.org/10.5194/ascmo-6-115-2020, 2020
Short summary
Short summary
Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
Donald P. Cummins, David B. Stephenson, and Peter A. Stott
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, https://doi.org/10.5194/ascmo-6-91-2020, https://doi.org/10.5194/ascmo-6-91-2020, 2020
Short summary
Short summary
We have developed a novel and fast statistical method for diagnosing effective radiative forcing (ERF), a measure of the net effect of greenhouse gas emissions on Earth's energy budget. Our method works by inverting a recursive digital filter energy balance representation of global climate models and has been successfully validated using simulated data from UK Met Office climate models. We have estimated time series of historical ERF by applying our method to the global temperature record.
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77, https://doi.org/10.5194/ascmo-6-61-2020, https://doi.org/10.5194/ascmo-6-61-2020, 2020
Short summary
Short summary
Extremes in weather can have lasting effects on human health and resource consumption. Studying the recurrence of these events on a regional scale can improve response times and provide insight into a changing climate. We introduce a set of clustering tools that allow for regional clustering of weather recordings from stations across Germany. We use these clusters to form regional models of summer temperature extremes and find an increase in the mean from 1960 to 2018.
Sophie C. Lewis, Sarah E. Perkins-Kirkpatrick, and Andrew D. King
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 133–146, https://doi.org/10.5194/ascmo-5-133-2019, https://doi.org/10.5194/ascmo-5-133-2019, 2019
Short summary
Short summary
Extreme temperature and precipitation events in Australia have caused significant socio-economic and environmental impacts. Determining the factors contributing to these extremes is an active area of research. This paper describes a set of studies that have examined the causes of extreme climate events in recent years in Australia. Ideally, this review will be useful for the application of these extreme event attribution approaches to climate and weather extremes occurring elsewhere.
Raquel Barata, Raquel Prado, and Bruno Sansó
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 67–85, https://doi.org/10.5194/ascmo-5-67-2019, https://doi.org/10.5194/ascmo-5-67-2019, 2019
Matz A. Haugen, Michael L. Stein, Ryan L. Sriver, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 37–55, https://doi.org/10.5194/ascmo-5-37-2019, https://doi.org/10.5194/ascmo-5-37-2019, 2019
Short summary
Short summary
This work uses current temperature observations combined with climate models to project future temperature estimates, e.g., 100 years into the future. We accomplish this by modeling temperature as a smooth function of time both in the seasonal variation as well as in the annual trend. These smooth functions are estimated at multiple quantiles that are all projected into the future. We hope that this work can be used as a template for how other climate variables can be projected into the future.
Rasmus E. Benestad, Bob van Oort, Flavio Justino, Frode Stordal, Kajsa M. Parding, Abdelkader Mezghani, Helene B. Erlandsen, Jana Sillmann, and Milton E. Pereira-Flores
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 37–52, https://doi.org/10.5194/ascmo-4-37-2018, https://doi.org/10.5194/ascmo-4-37-2018, 2018
Short summary
Short summary
A new study indicates that heatwaves in India will become more frequent and last longer with global warming. Its results were derived from a large number of global climate models, and the calculations differed from previous studies in the way they included advanced statistical theory. The projected changes in the Indian heatwaves will have a negative consequence for wheat crops in India.
Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36, https://doi.org/10.5194/ascmo-4-19-2018, https://doi.org/10.5194/ascmo-4-19-2018, 2018
Short summary
Short summary
We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Ralf Lindau and Victor Karel Christiaan Venema
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 1–18, https://doi.org/10.5194/ascmo-4-1-2018, https://doi.org/10.5194/ascmo-4-1-2018, 2018
Short summary
Short summary
Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.
Erik Fraza, James B. Elsner, and Thomas H. Jagger
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 105–114, https://doi.org/10.5194/ascmo-2-105-2016, https://doi.org/10.5194/ascmo-2-105-2016, 2016
Short summary
Short summary
Climate influences on hurricane intensification are investigated by averaging hourly intensification rates over the period 1975–2014 in 8° by 8° latitude–longitude grid cells. The statistical effects of hurricane intensity, sea-surface temperature (SST), El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Madden–Julian Oscillation (MJO) are quantified. Intensity, SST, and NAO had a positive effect on intensification rates. The NAO effect should be further studied.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
Short summary
Short summary
In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
Cited articles
Altman, D. G. and Bland, J. M.: Measurement in medicine: The analysis of
method comparison studies, Statistician, 32, 307–317, 1983. a
Bennett, J. C., Grose, M. R., Corney, S. P., White, C. J., Holz, G. K.,
Katzfey, J. J., Post, D. A., and Bindoff, N. L.: Performance of an empirical
bias-correction of a high-resolution climate dataset, Int. J. Climatol., 34,
2189–2204, https://doi.org/10.1002/joc.3830, 2014. a, b
Bentamy, A., Piollé, J.-F., Grouazel, A., Danielson, R. E., Gulev, S. K.,
Paul, F., Azelmat, H., Mathieu, P.-P., von Schuckmann, K., Sathyendranath,
S., Evers-King, H., Esau, I., Johannessen, J. A., Clayson, C. A., Pinker,
R. T., Grodsky, S. A., Bourassa, M., Smith, S. R., Haines, K., Valdivieso,
M., Merchant, C. J., Chapron, B., Anderson, A., Hollmann, R., and Josey,
S. A.: Review and assessment of latent and sensible heat flux accuracy over
global oceans, Remote Sens. Environ., 201, 196–218,
https://doi.org/10.1016/j.rse.2017.08.016, 2017. a
Berrisford, P., Dee, D., Poli, P., Brugge, R., Fielding, K., Fuentes, M.,
Kållberg, P., Kobayashi, S., Uppala, S., and Simmons, A.:
The ERA-Interim archive Version 2.0, ERA Report Series No. 1,
available at: https://www.ecmwf.int/en/elibrary/8174-era-interim-archive-version-20 (last access: October 2018),
2011. a
Beven, K.: Towards a methodology for testing models as hypotheses in the
inexact sciences, P. Roy. Soc. A-Math. Phy., 475, 1–19, https://doi.org/10.1098/rspa.2018.0862,
2019. a
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B.: Julia: A fresh
approach to numerical computing, SIAM Rev., 59, 65–98,
https://doi.org/10.1137/141000671, 2017. a
Bland, J. M. and Altman, D. G.: Statistical methods for assessing agreement
between two methods of clinical measurement, Lancet, 327, 307–310,
https://doi.org/10.1016/S0140-6736(86)90837-8, 1986. a
Cannon, A. J.: Multivariate bias correction of climate model output: Matching
marginal distributions and intervariable dependence structure, J. Climate,
29, 7045–7064, https://doi.org/10.1175/JCLI-D-15-0679.1, 2016. a, b
Cannon, A. J.: Multivariate quantile mapping bias correction: An N-dimensional
probability density function transform for climate model simulations of
multiple variables, Clim. Dynam., 50, 31–49,
https://doi.org/10.1007/s00382-017-3580-6, 2018. a, b
Claxton, B. M.: Using a neural network to benchmark a diagnostic
parameterization: The Met Office’s visibility scheme, Q. J. Roy.
Meteor. Soc., 134, 1527–1537, 2008. a
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011. a, b
Danielson, R. E., Zhang, M., and Perrie, W. A.: Collocations of
selected in situ (ICOADS) observations and five centred samples of analysis
(ERA Interim) and global (HadGEM2) and regional (WRF) numerical model surface
marine data, Canadian Cryospheric Information Network online data archive, https://doi.org/10.21963/13169, 2020. a, b
Dee, D. P.: Bias and data assimilation, Q.. J. Roy. Meteor. Soc., 131,
3323–3343, https://doi.org/10.1256/qj.05.137, 2005. a, b
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011. a
Dorman, C. E., Mejia, J., Koračin, D., and McEvoy, D.:
Worldwide marine fog occurrence and climatology, Marine
Fog: Challenges and Advancements in Observations, Modeling, and
Forecasting, edited: Koračin, D. and Dorman, C. E., Springer, 7–152,
https://doi.org/10.1007/978-3-319-45229-6_2, 2017. a, b, c
Freeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel,
W. E., Berry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z.,
Lawrimore, J., Rayner, N. A., Rosenhagen, G., and Smith, S. R.: ICOADS
Release 3.0: A major update to the historical marine climate record, Int. J.
Climatol., 37, 2211–2232, https://doi.org/10.1002/joc.4775, 2017. a
Freilich, M. H. and Challenor, P. G.: A new approach for determining fully
empirical altimeter wind speed model functions, J. Geophys. Res., 99,
25051–25062, https://doi.org/10.1029/94JC01996, 1994. a, b, c
Fuller, W. A.: Errors in variables, Encyclopedia of
Statistical Sciences, edited by: Kotz, S., Read, C. B., Balakrishnan, N., Vidakovic, B. and
Johnson, N. L., https://doi.org/10.1002/0471667196.ess1036.pub2, 2006. a
Glisan, J. M., Gutowski Jr., W. J., Cassano, J. J., and Higgins, M. E.:
Effects of spectral nudging in WRF on Arctic temperature and precipitation
simulations, J. Climate, 26, 3985–3999, https://doi.org/10.1175/JCLI-D-12-00318.1,
2013. a
Gultepe, I., Pagowski, M., and Reid, J.: A satellite-based fog detection scheme
using screen air temperature, Weather Forecast., 22, 444–456,
https://doi.org/10.1175/WAF1011.1, 2007. a
Gultepe, I., Zhou, B., Milbrandt, J., Bott, A., Li, Y., Heymsfield, A. J.,
Ferrier, B., Ware, R., Pavolonis, M., Kuhn, T., Gurka, J., Liu, P., and
Cermak, J.: A review on ice fog measurements and modeling, Atmos. Res.,
151, 2–19, https://doi.org/10.1016/j.atmosres.2014.04.014, 2015. a
Gultepe, I., Milbrandt, J. A., and Zhou, B.: Marine fog: A review
on microphysics and visibility prediction, Marine Fog: Challenges and
Advancements in Observations, Modeling, and Forecasting, edited by: Koračin, D.
and Dorman, C. E., Springer, 345–394,
https://doi.org/10.1007/978-3-319-45229-6_7, 2017. a, b, c, d, e
Gultepe, I., Sharman, R., Williams, P. D., Zhou, B., Ellrod, G., Minnis, P.,
Trier, S., Griffin, S., Yum, S. S., Gharabaghi, B., Feltz, W., Temimi, M.,
Pu, Z., Storer, L. N., Kneringer, P., Weston, M. J., Chuang, H.-Y., Thobois,
L., Dimri, A. P., Dietz, S. J., França, G. B., Almeida, M. V., and
Albquerque Neto, F. L.: A review of high impact weather for aviation
meteorology, Pure Appl. Geophys., 176, 1869–1921,
https://doi.org/10.1007/s00024-019-02168-6, 2019. a
Hall, M. J. W.: Local deterministic model of singlet state correlations based
on relaxing measurement independence, Phys. Rev. Lett., 105,
https://doi.org/10.1103/PhysRevLett.105.250404, 2010. a
Haraway, D.: Situated knowledges: The science question in feminism and the
privilege of partial perspective, Feminist Stud., 14, 575–599, 1988. a
Hasenauer, S.: Bias correction of soil moisture time series from
ERS scatterometer and ERA-Interim data for a global 10-year period,
EUMETSAT Meteorological Satellite Conference, Córdoba, Spain,
available at:
https://www.eumetsat.int/website/home/News/ConferencesandEvents/PreviousEvents/DAT_2042511.html (last access: June 2017),
2010. a, b, c
Hines, K. M., Bromwich, D. H., Bai, L., Bitz, C. M., Powers, J. G., and
Manning, K. W.: Sea ice enhancements to Polar WRF, Mon. Weather Rev., 143,
2363–2385, https://doi.org/10.1175/MWR-D-14-00344.1, 2015. a
IPCC: Summary for Policymakers, in: Climate Change 2013: The
Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmenta Panel on Climate Change, edited by:
Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press,
2013. a, b
Janjić, T., N, N. B., Bocquet, M., Carton, J. A., Cohn, S. E., Dance,
S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A., and Weston,
P.: On the representation error in data assimilation, Q. J. Roy.
Meteor. Soc., 144, 1257–1278, https://doi.org/10.1002/qj.3130, 2018. a
Jones, C. D., Hughes, J. K., Bellouin, N., Hardiman, S. C., Jones, G. S., Knight, J., Liddicoat, S., O'Connor, F. M., Andres, R. J., Bell, C., Boo, K.-O., Bozzo, A., Butchart, N., Cadule, P., Corbin, K. D., Doutriaux-Boucher, M., Friedlingstein, P., Gornall, J., Gray, L., Halloran, P. R., Hurtt, G., Ingram, W. J., Lamarque, J.-F., Law, R. M., Meinshausen, M., Osprey, S., Palin, E. J., Parsons Chini, L., Raddatz, T., Sanderson, M. G., Sellar, A. A., Schurer, A., Valdes, P., Wood, N., Woodward, S., Yoshioka, M., and Zerroukat, M.: The HadGEM2-ES implementation of CMIP5 centennial simulations, Geosci. Model Dev., 4, 543–570, https://doi.org/10.5194/gmd-4-543-2011, 2011. a, b
Koračin, D.: Modeling and forecasting marine fog, Marine
Fog: Challenges and Advancements in Observations, Modeling, and
Forecasting, edited by: Koračin, D. and Dorman, C. E. Springer, 425–475,
https://doi.org/10.1007/978-3-319-45229-6_9, 2017. a, b
Lorenc, A. C.: Analysis methods for numerical weather prediction, Q. J.
Roy. Meteor. Soc., 122, 1177–1194, 1986. a
Mahfouf, J.-F.: Analysis of soil moisture from near-surface parameters: A
feasibility study, J. Appl. Meteorol., 30, 1534–1547,
https://doi.org/10.1175/1520-0450(1991)030<1534:AOSMFN>2.0.CO;2, 1991. a, b, c
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van
Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A.,
Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer,
R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next
generation of scenarios for climate change research and assessment, Nature,
463, 747–756, https://doi.org/10.1038/nature08823, 2010. a
NOAA: National Weather Service Observing Handbook No. 1, Marine
surface weather observations, available at:
http://www.vos.noaa.gov (last access: August 2018), May 2010. a
Oreskes, N., Shrader-Frechette, K., and Belitz, K.: Verification, validation,
and confirmation of numerical models in the Earth sciences, Science, 263,
641–646, https://doi.org/10.1126/science.263.5147.641, 1994. a, b, c
Parker, W. S.: Reanalyses and observations: What's the difference?, B.
Am. Meteorol. Soc., 97, 1565–1572, https://doi.org/10.1175/BAMS-D-14-00226.1, 2016. a
Pearson, K.: On the mathematical theory of errors of judgement, with special
reference to the personal equation, Philos. T. R. Soc., 198,
235–299, 1902. a
Simmons, A. J., Jones, P. D., da Costa Bechtold, V., Beljaars, A. C. M.,
Kållberg, P. W., Saarinen, S., Uppala, S. M., Viterbo, P., and Wedi, N.:
Comparison of trends and low-frequency variability in CRU, ERA-40 and
NCEP/NCAR analyses of surface air temperature, J. Geophys. Res., 109, 1–18,
https://doi.org/10.1029/2004JD005306, 2004. a, b, c, d
Simmons, A. J., Willett, K. M., Jones, P. D., Thorne, P. W., and Dee, D. P.:
Low-frequency variations in surface atmospheric humidity, temperature, and
precipitation: Inferences from reanalyses and monthly gridded observational
data sets, J. Geophys. Res., 115, 1–21, https://doi.org/10.1029/2009JD012442, 2010. a, b, c
Tardif, R.: Precipitation and fog, Marine Fog: Challenges and
Advancements in Observations, Modeling, and Forecasting, edited by: Koračin, D.
and Dorman, C. E., Springer, 395–423,
https://doi.org/10.1007/978-3-319-45229-6_8, 2017.
a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the
experiment design, B. Am. Meteorol. Soc., 93, 485–498,
https://doi.org/10.1175/JTECH-D-12-00008.1, 2012. a, b, c, d
van Oldenborgh, G. J., Yiou, P., and Vautard, R.: On the roles of circulation and aerosols in the decline of mist and dense fog in Europe over the last 30 years, Atmos. Chem. Phys., 10, 4597–4609, https://doi.org/10.5194/acp-10-4597-2010, 2010. a, b
Wilkinson, J. M., Porson, A. N. F., Bornemann, F. J., Weeks, M., Field, P. R.,
and Lock, A. P.: Improved microphysical parametrization of drizzle and fog
for operational forecasting using the Met Office Unified Model, Q. J.
Roy. Meteor. Soc., 139, 488–500, https://doi.org/10.1002/qj.1975, 2013. a
Zhang, S. and Lewis, J. M.: Synoptic processes, Marine Fog:
Challenges and Advancements in Observations, Modeling, and Forecasting, edited by: Koračin, D. and Dorman, C. E., Springer, 291–343,
https://doi.org/10.1007/978-3-319-45229-6_6, 2017. a
Short summary
Visibility is estimated for the 21st century using global and regional climate model output. A baseline decrease in visibility in the Arctic (10 %) is more notable than in the North Atlantic (< 5 %). We develop an adjustment that yields greater consistency among models and explore the justification of our ad hoc adjustment toward ship observations during the historical period. Baseline estimates are found to be sensitive to the representation of temperature and humidity.
Visibility is estimated for the 21st century using global and regional climate model output. A...
