weather over recent centuries, and thus to examine how
meteorological statistics (regimes, extremes etc.) respond to
forcings over the long-term. The confrontation of models with
observations, within the context of extremes, becomes a crucial issue, involving complex statistical theories. These
mathematical tools allow for the regionalization of the climate,
with the aim of forecasting climate variables on a very small
scale (e.g. precipitation at the scale of a city or a field) based on
understanding of large-scale variables (such as atmospheric
circulation on the scale of the North Atlantic basin).
Detection and Attribution of Climate Change
Understanding the role of human activity in climate change
has become a major issue in recent years. The identification
of this role is not limited to superimposing demographic,
greenhouse gas and temperature curves on each other and
highlighting their simultaneous growth. The difficulty in
assessing this role is that there is no ‘control’ version of the
Earth on which the climate with no human activity can be
evaluated.
To attribute climate change to any one factor, it is necessary to evaluate the role of all other possible factors and to
show that the characteristics of the change are compatible
with this factor alone. This endeavor is usually done by
researchers through climate model simulations, taking into
account all of the physical factors minus one, and then
comparing the simulations obtained with the observations.
The key is to then identify the crucial factor(s) that best allows
simulations to resemble observations. The difficulty with this
evaluation is defining the statistical criteria for the comparisons and to obtain the necessary confidence intervals.
In order to make progress in this direction, improved
models able to take the many interactions in the climate
system into account are essential. This requires considering
not only physical interactions involving energy and water
cycles, but also the interactions between climate and the
biogeochemical cycles. In particular, land use, deforestation,
different types of aerosols (natural and anthropic) are often
excluded from climate simulations. The distinction between
meteorology and changes in average climate also necessitates improvement in the resolution of the models used,
which goes hand in hand with better representation of
small-scale processes. All of these elements combined with
high-resolution data make it possible to better understand the
risks associated with the profound changes in our environment linked to the ongoing global warming.
The attribution of extreme events is a rather recent field of
research, that poses many scientific challenges (NAS 2016).
Since 2012, the Bulletin of the American Meteorological
Society (BAMS) publishes a summary of the analyses of
past extreme events and their attribution to climate change.
One of the teams has developed the weather@home system
(Massey et al. 2015), which was devised to calculate tens of
thousands of high resolution climate simulations. Those
large ensembles are performed with present day greenhouse
gas concentration and natural variability, and with natural
variability only. They allow estimating the probability density function that a key climate variable (temperature, precipitation) exceeds a large threshold, with and without
climate change. Other statistical methods have been devised
since then to attribute extreme events to climate change
(Stott et al. 2016; Jézéquel et al. 2018).
References
Adams, J., et al. (2003). Proxy Evidence for an El Niño-like Response
to Volcanic Forcing. Nature, 426(6964), 274–278.
Bjerknes, J. (1969). Atmospheric teleconnections from equatorial
pacific. Monthly Weather Review, 97(3), 163–172.
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J.,
Masson-Delmotte, V., Abe-Ouchi, A., et al. (2012). Evaluation of
climate models using palaeoclimatic data. Nature Climate Change,
2, 417–424. https://doi.org/10.1038/nclimate1456.
Braconnot, P., Joussaume, S., Marti, O., & de Noblet, N. (1999).
Synergistic feedbacks from ocean and vegetation on the African
monsoon response to mid-Holocene insolation. Geophysical
Reseach Letters, 26, 2481–2484.
Braconnot, P., et al. (2007a). Results of PMIP2 coupled simulations of
the mid-holocene and last glacial maximum—Part 1: Experiments
and large-scale features. Climate of the Past, 3(2), 261–277.
Braconnot, P., et al. (2007b). Results of PMIP2 coupled simulations of
the mid-holocene and last glacial maximum—Part 2: Feedbacks
with emphasis on the location of the ITCZ and mid- and high
latitudes heat budget. Climate of the Past, 3(2), 279–296.
Bradley, R. S. (1999). Paleoclimatology: Reconstructing climates of
the quaternary, 2nd edn. San Diego: Harcourt/Academic, 613p.
Brazdil, R., et al. (2005). Historical climatology in Europe—The state
of the art. Climatic Change, 70(3), 363–430.
Cane, M., et al. (2006). Progress in paleoclimate modeling. Journal of
Climate, 19(20), 5031–5057.
Carre, M., Sachs, J. P., Purca, S., Schauer, A. J., Braconnot, P., Falcon,
R. A., et al. (2014). Holocene history of ENSO variance and
asymmetry in the eastern tropical Pacific. Science, 345, 1045–1048.
Claussen, M., et al. (1999). Simulation of an Abrupt change in saharan
vegetation in the mid-holocene. Geophysical Research Letters, 26
(14), 2037–2040.
Claussen, M., & Gayler, V. (1997). The greening of the sahara during
the mid-holocene: Results of an interactive atmosphere-biome
model. Global Ecology and Biogeography Letters, 6, 369–377.
Clement, A. C., et al. (2000). Suppression of El Nino during the
mid-holocene by changes in the earth’s orbit. Paleoceanography,
15(6), 731–737.
Cobb, K. M., Westphal, N., Sayani, H. R., Watson, J. T., Di Lorenzo,
E., Cheng, H., et al. (2013). Highly variable El Nino-southern
oscillation throughout the holocene. Science, 339, 67–70.
Coles, S. (2001). An introduction to statistical modeling of extreme
values (p. 208). London, New York: Springer.
Crucifix, M., et al. (2002). Climate evolution during the holocene: A
study with an earth system model of intermediate complexity.
Climate Dynamics, 19(1), 43–60.
de Noblet, N., et al. (1996). Possible role of atmosphere-biosphere
interactions in triggering the last glaciation. Nature, 23(22), 3191–3194.
30 An Introduction to the Holocene and Anthropic Disturbance
441
meteorological statistics (regimes, extremes etc.) respond to
forcings over the long-term. The confrontation of models with
observations, within the context of extremes, becomes a crucial issue, involving complex statistical theories. These
mathematical tools allow for the regionalization of the climate,
with the aim of forecasting climate variables on a very small
scale (e.g. precipitation at the scale of a city or a field) based on
understanding of large-scale variables (such as atmospheric
circulation on the scale of the North Atlantic basin).
Detection and Attribution of Climate Change
Understanding the role of human activity in climate change
has become a major issue in recent years. The identification
of this role is not limited to superimposing demographic,
greenhouse gas and temperature curves on each other and
highlighting their simultaneous growth. The difficulty in
assessing this role is that there is no ‘control’ version of the
Earth on which the climate with no human activity can be
evaluated.
To attribute climate change to any one factor, it is necessary to evaluate the role of all other possible factors and to
show that the characteristics of the change are compatible
with this factor alone. This endeavor is usually done by
researchers through climate model simulations, taking into
account all of the physical factors minus one, and then
comparing the simulations obtained with the observations.
The key is to then identify the crucial factor(s) that best allows
simulations to resemble observations. The difficulty with this
evaluation is defining the statistical criteria for the comparisons and to obtain the necessary confidence intervals.
In order to make progress in this direction, improved
models able to take the many interactions in the climate
system into account are essential. This requires considering
not only physical interactions involving energy and water
cycles, but also the interactions between climate and the
biogeochemical cycles. In particular, land use, deforestation,
different types of aerosols (natural and anthropic) are often
excluded from climate simulations. The distinction between
meteorology and changes in average climate also necessitates improvement in the resolution of the models used,
which goes hand in hand with better representation of
small-scale processes. All of these elements combined with
high-resolution data make it possible to better understand the
risks associated with the profound changes in our environment linked to the ongoing global warming.
The attribution of extreme events is a rather recent field of
research, that poses many scientific challenges (NAS 2016).
Since 2012, the Bulletin of the American Meteorological
Society (BAMS) publishes a summary of the analyses of
past extreme events and their attribution to climate change.
One of the teams has developed the weather@home system
(Massey et al. 2015), which was devised to calculate tens of
thousands of high resolution climate simulations. Those
large ensembles are performed with present day greenhouse
gas concentration and natural variability, and with natural
variability only. They allow estimating the probability density function that a key climate variable (temperature, precipitation) exceeds a large threshold, with and without
climate change. Other statistical methods have been devised
since then to attribute extreme events to climate change
(Stott et al. 2016; Jézéquel et al. 2018).
References
Adams, J., et al. (2003). Proxy Evidence for an El Niño-like Response
to Volcanic Forcing. Nature, 426(6964), 274–278.
Bjerknes, J. (1969). Atmospheric teleconnections from equatorial
pacific. Monthly Weather Review, 97(3), 163–172.
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J.,
Masson-Delmotte, V., Abe-Ouchi, A., et al. (2012). Evaluation of
climate models using palaeoclimatic data. Nature Climate Change,
2, 417–424. https://doi.org/10.1038/nclimate1456.
Braconnot, P., Joussaume, S., Marti, O., & de Noblet, N. (1999).
Synergistic feedbacks from ocean and vegetation on the African
monsoon response to mid-Holocene insolation. Geophysical
Reseach Letters, 26, 2481–2484.
Braconnot, P., et al. (2007a). Results of PMIP2 coupled simulations of
the mid-holocene and last glacial maximum—Part 1: Experiments
and large-scale features. Climate of the Past, 3(2), 261–277.
Braconnot, P., et al. (2007b). Results of PMIP2 coupled simulations of
the mid-holocene and last glacial maximum—Part 2: Feedbacks
with emphasis on the location of the ITCZ and mid- and high
latitudes heat budget. Climate of the Past, 3(2), 279–296.
Bradley, R. S. (1999). Paleoclimatology: Reconstructing climates of
the quaternary, 2nd edn. San Diego: Harcourt/Academic, 613p.
Brazdil, R., et al. (2005). Historical climatology in Europe—The state
of the art. Climatic Change, 70(3), 363–430.
Cane, M., et al. (2006). Progress in paleoclimate modeling. Journal of
Climate, 19(20), 5031–5057.
Carre, M., Sachs, J. P., Purca, S., Schauer, A. J., Braconnot, P., Falcon,
R. A., et al. (2014). Holocene history of ENSO variance and
asymmetry in the eastern tropical Pacific. Science, 345, 1045–1048.
Claussen, M., et al. (1999). Simulation of an Abrupt change in saharan
vegetation in the mid-holocene. Geophysical Research Letters, 26
(14), 2037–2040.
Claussen, M., & Gayler, V. (1997). The greening of the sahara during
the mid-holocene: Results of an interactive atmosphere-biome
model. Global Ecology and Biogeography Letters, 6, 369–377.
Clement, A. C., et al. (2000). Suppression of El Nino during the
mid-holocene by changes in the earth’s orbit. Paleoceanography,
15(6), 731–737.
Cobb, K. M., Westphal, N., Sayani, H. R., Watson, J. T., Di Lorenzo,
E., Cheng, H., et al. (2013). Highly variable El Nino-southern
oscillation throughout the holocene. Science, 339, 67–70.
Coles, S. (2001). An introduction to statistical modeling of extreme
values (p. 208). London, New York: Springer.
Crucifix, M., et al. (2002). Climate evolution during the holocene: A
study with an earth system model of intermediate complexity.
Climate Dynamics, 19(1), 43–60.
de Noblet, N., et al. (1996). Possible role of atmosphere-biosphere
interactions in triggering the last glaciation. Nature, 23(22), 3191–3194.
30 An Introduction to the Holocene and Anthropic Disturbance
441