Comptes Rendus Géoscience

Tim Arnold

Tamara Markovic

Guy J.D. Kirk

Maria Schönbächler

Mark Rehkämper

Fangjie J. Zhao

Dominik J. Weiss

Iron and zinc isotope fractionation during uptake and translocation in rice (<i>Oryza sativa</i>) grown in oxic and anoxic soils

Maud H. Devès

Sally Reynolds

Geoffrey C.P. King

Simon Kuebler

Derek Sturdy

Nan Godet

Handled by Yann Klinger

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C. R. Geoscience 347 (2015) 159–160
http
163eword
ctonics of the Levant fault system
In June 2013, for the second time, an international
rkshop dedicated to the tectonics of the Levant fault
tem and the Arabic plate was held in Paris. During two
s, this meeting gathered researchers from 19 institu-
s and 12 countries, with 24 presentations. During this
eting, a large variety of topics were addressed, ranging
 new insights into the geodynamics of the Red Sea to
thquake history along the Dead Sea fault. A key point of
 meeting was actually to gather contributions focused
the same object, here the Levant fault system, but with
erent perspectives, to foster new collaborations and
earch projects. In line with this idea, several presenta-
s were actually dealing with issues related to the
aeoclimate of this specific region, engineering issues
ut earthquake destructions, or the impact of the Dead
 active tectonics on the evolution of hominins, aside
 general tectonics.
At the end of this meeting, the journal Comptes rendus
science offered to publish some of the contributions,
r a classical peer-review process. As a result, five
tributions are presented in this issue, giving a sense of
 variety of the topics addressed during this meeting.
The first contribution, by Frédéric Masson et al. (Masson
l., 2015), describes a new velocity field determined in
 region located south of the Dead Sea from repeated GPS
veys. Although their results are consistent with
viously published GPS measurements, increased reso-
on allows the authors to address the matter of locking
th, critical to earthquake hazard assessment, in more
ail, because the different surveys encompass a longer
e period.
Then two contributions address issues related to the
ord of historical earthquakes through the destruction of
torical buildings. Yacine Benjelloun et al. (Benjelloun et
2015) document details of the material used to build,
 potentially rebuild after an earthquake, an aqueduct in
Turkey, at the northern end of the Dead Sea fault. Miklós
Kázmér et al. (Kázmér and Major, 2015), along the same
line, report damage to several buildings along the Ghab
basin, in Syria, which are attributed to some historical
earthquakes. Details of the destructions and damage in a
nearby stone quarry are given, which confirm the
earthquake origin of destructions.
A contribution by Yossi Mart et al. (Mart and Vachtman,
2015) discusses the geodynamics of the Dead Sea fault to
emphasize its extensional component. Despite the fact that
they do not necessarily support the most consensual view
of the regional geodynamics, Mart and his colleagues
present solid field evidence that need to be incorporated
into the regional geodynamical models.
The last contribution, by Maud Hélène Devès et al.
(Devès et al., 2015), addresses the interaction between
evolving landscapes and the evolution of hominins in East
Africa and in the Levant region. They point how the
distribution of key minerals in soil, essential to the growth
of a mega fauna in the region, was driving the hominins’
survival strategies in these areas.
Eventually, these five contributions give a nice sam-
pling of all topics discussed during the 2013 ‘‘Levant
meeting’’, and are solid scientific contributions to the field
of earth sciences.
Yann Klinger, as the organizer of the ‘‘Levant fault’’
meeting, wants to thank Vincent Courtillot, for having
offered to publish some of the results presented during the
meeting in the Comptes rendus Geoscience.
References
Benjelloun, Y., Sigoyer, J. (de), Carlut, J., Hubert-Ferrari, A., Dessales, H.,
Pamir, H., Karabacak, V., 2015. Characterization of building materials
from the aqueduct of Antioch-on-the-Orontes (Turkey). C.R. Geosci-
ence 347, (this issue).
Contents lists available at ScienceDirect
Comptes Rendus Geoscience
ww w.s c ien c edi r ec t . c om
://dx.doi.org/10.1016/j.crte.2015.06.0081-0713/ 2015 Published by Elsevier Masson SAS on behalf of Académie des sciences.
Foreword / C. R. Geoscience 347 (2015) 159–160160Devès, M.H., Reynolds, S., King, G.C.P., Sturdy, D., Godet, N., 2015. Insights
from Earth Sciences into Human Evolution studies: the example of
prehistoric landscape use in Africa and the Levant. C.R. Geoscience
347, (this issue).
Kázmér, M., Major, B., 2015. Safita castle and rockfalls in the ‘‘dead
villages’’ of coastal Syria – an archaeseismological study. C.R. Geosci-
ence 347, (this issue).
Mart, Y., Vachtman, D., 2015. The geodynamic interaction between the
Neogene rivers and the segmented Levant rift system. C.R. Geoscience
347, (this issue).
Masson, F., Hamiel, Y., Agnon, A., Klinger, Y., 2015. Variable behavior of the
Dead Sea Fault along the southern Arava segment from GPS measu-
rements. C.R. Geoscience 347, (this issue).
Guest Editor
Yann Klinger
Tectonique et mécanique de la lithosphère – Sismologie,
Institut de physique du globe de Paris, 1, rue Jussieu,
75238 Paris cedex 05, France
E-mail address: klinger@ipgp.fr
Available online 8 August 2015


Insights from Earth Sciences into Human Evolution studies: The example of prehistoric landscape use in Africa and the Levant

Daniel Petit

Alain Véron

Pascal Flament

Karine Deboudt

André Poirier

Review of pollutant lead decline in urban air and human blood: A case study from northwestern Europe

CV2 - Editorial board ENG

Raphaelle Escoube

Olivier J. Rouxel

Oleg S. Pokrovsky

Andrew Schroth

Robert Max Holmes

Olivier F.X. Donard

Iron isotope systematics in Arctic rivers

Miklós Kázmér

Balázs Major

Sāfitā castle and rockfalls in the ‘dead villages’ of coastal Syria – an archaeoseismological study

Kurt Kyser

Larry Lahusen

Garth Drever

Colin Dunn

Evelyne Leduc

Don Chipley

For
Ge
ac
(1
ge
fact
ma
atm
con
but
dem
hum
pre
ant
imp
our
too
larl
fact
soc
res
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C. R. Geoscience 347 (2015) 213–214
http
163eword
ochemical and isotopic record of anthropogenic
tivities – Thematic issue dedicated to Jean Carignan
965–2012). Part 1: Radiogenic isotopes and elemental
ochemistry
Anthropogenic activities are today one of the major
ors controlling the evolution of our environment with
jor consequences on all Earth surface compartments:
osphere, oceans, continents and biosphere. Such
sequences accelerated with the industrial revolution,
 probably started much earlier as they are related to the
ographic evolution and the settlement of various
an communities during the last millennia. They are
sently reaching unequal effects all over the globe.
Defining precisely present-day and past impacts of
hropogenic activities and forecasting their future
act requires tools to assess their specific effects on
 environment. Elemental geochemical and isotopic
ls developed in the geosciences appear to be particu-
y useful for tracing anthropogenic activities.
Today research in this field is in full growth due to the
 that the scientific issues it addresses are important
ietal issues. It is also related to the fact that novel
earch prospects become achievable thanks to the recent
lytical developments of instruments such as MC-
MS, nano-SIMS, etc. All of these studies lead today to
el orientations whose scientific potentialities but also
ir usefulness as decision-making tools should be better
wn and popularized. It is one of the goals of this
matic issue of C. R. Geoscience, which assesses the
acts of anthropogenic activity on the environment. The
e gathers 19 papers by colleagues specializing in
chemical and isotopic tracing tools and methods. The
ers demonstrate how diversified are the approaches
 geochemical tools used in a rapidly developing field,
ich combines classical geochemical tools with novel
chemical tracers such as the so-called ‘‘non-traditional
le isotopes’’.
This issue is divided into two volumes: the first one
gathers radiogenic and elemental geochemistry approa-
ches, whereas the second one is devoted to the use of stable
isotopes, including both traditional (C, H, O, N, S) and non-
traditional isotopes.
This first volume is comprised of ten papers, the first six of
which use Pb isotopes as tracers of anthropogenic sources to
the environment as well as tools for metallogenic explora-
tion. The paper by Kyser et al. (2015) demonstrates the
usefulness of Pb isotopes in surface media to ‘‘distinguish
anthropogenic sources from undercover uranium sources.’’
The second paper, by Stevenson et al. (2015), points out how
Pb, Sr, and Nd isotope systematics in marine sediments help
understanding ocean current changes and sedimentary
dynamics of the Arctic Ocean. The following four papers
present several case studies demonstrating the usefulness of
Pb isotope studies to identify the source of anthropogenic Pb
in continental ecosystems. Négrel et al. (2015) show the
potential of such studies at the scale of a volcanic basin. The
next two articles clearly demonstrate how geochemical
monitoring helps assessing the evolution of anthropogenic
impacts on the environment and human health. For instance,
Petit et al. (2015) show a comparison between blood Pb
isotope analyses of two populations from the same region
and sampled 30 years apart. They illustrate the impact on
humans of measures taken to reduce Pb atmospheric
pollution level, while showing the need to take into account
other sources of Pb and also the human Pb metabolism to
correctly interpret the presented results. Cloquet et al.
(2015) point out that lichens can be useful biomonitors of
atmospheric Pb pollution levels and source appointment in a
given region. On the other hand, Hoàng-Hóa et al. (2015)
show how passive samplers can help monitoring a
geochemical atmospheric evolution at relatively small time
Contents lists available at ScienceDirect
Comptes Rendus Geoscience
ww w.s c ien c edi r ec t . c om
://dx.doi.org/10.1016/j.crte.2015.09.0011-0713/ 2015 Académie des sciences. Published by Elsevier Masson SAS. All rights reserved.
Foreword / C. R. Geoscience 347 (2015) 213–214214scales. In addition, this article shows the usefulness of Pb
isotopes coupled with Nd and Sr isotopes to decipher the
evolution through time of the various sources of atmospher-
ic dust at a local scale. The following three articles present
examples of other isotopic and geochemical tools for
identifying both the source and fate of anthropogenic
products in the environment. Gogot et al. (2015) show the
potential of the infrequently used Os isotopes as tracers of
atmospheric emissions generated by aluminum smelters.
Merschel et al. (2015) and Hissler et al. (2015) highlight how
rare-earth elements patterns and geochemical ‘‘anomalies’’
in some elements of this chemical family can trace
anthropogenic chemical fluxes. This first series of ten papers
closes with the work by Pédrot et al. (2015) demonstrating
how helpful are geochemical observations at smaller and
smaller scales ranging from micrometric to nanometric for
studying the biochemical processes that control pollutant
mobility in the environment, such as in this case study. All
these papers, as well as those presented in the second
volume of the thematic issue (see Chabaux et al., 2015),
clearly demonstrate how dynamic the field of isotope
geoscience is, and how the various geochemical tools can
be applied to tackle a variety of environmental issues.
We would like to dedicate the two volumes of the
thematic issue of C. R. Geoscience to our colleague and
friend Jean Carignan (1965–2012). Jean (Fig. 1) was among
the pioneers of non-traditional stable isotope applications
to the environmental geosciences, during his too short
scientific career.
A few years ago, we initiated, with Jean, the idea of
putting together contributions about this topic in a
thematic issue of C. R. Geoscience. However, Jean’s disease
and then death put a stop to our project. Today we are
proud to have honored this initiative and to have been able
to finalize this volume thanks to the participation of many
of Jean’s friends and colleagues. We warmly thank all of
them as well as those who reviewed the manuscripts1. We
are convinced that the present thematic issue will make a
lasting contribution to popularizing the development of
geochemical tracer studies for environmental issues,
which Jean had set his heart on. It will probably render
the most proper tribute to Jean’s work.
References
Chabaux, F., Sonke, J.E., Négrel, P., Cloquet, C., 2015. Foreword: geochem-
ical and isotopic record of anthropogenic activities. Part 2: traditional
and non-traditional stable isotopes. C. R. Geoscience 347 (this issue).
Cloquet, C., Estrade, N., Carignan, J., 2015. Ten years of elemental atmo-
spheric metal fallout and Pb isotopic composition monitoring using
lichens in north-eastern France. C. R. Geoscience 347 (this issue).
Gogot, J., Poirier, A., Boullemant, A., 2015. Osmium isotopic tracing of
atmospheric emissions from an aluminum smelter. C. R. Geoscience
347 (this issue).
Hissler, C., Hostache, R., Iffly, J.-F., Pfister, L., Stille, P., 2015. Anthropogenic
rare earth element fluxes to floodplains: coupling between geochem-
ical monitoring and hydrodynamic-sediment transport modelling. C.
R. Geoscience 347 (this issue).
Hoàng-Hóa, T.B., Stille, P., Dietze, V., Guéguen, F., Perrone, T., Gieré, R.,
2015. Pb, Sr and Nd isotopic composition and trace element charac-
teristics of airborne particles collected with passive shelters. C. R.
Geoscience 347 (this issue).
Kyser, K., Lahusen, L., Drever, G., Dunn, C., Leduc, E., Chipley, D., 2015.
Using Pb isotopes in surface media to distinguish anthropogenic
sources from undercover uranium sources. C. R. Geoscience 347 (this
issue).
Merschel, G., Bau, M., Baldewein, L., Dantas, E.-L., Waqlde, D., Bühn, B.,
2015. Tracing and tracking waste water-derived substances in fresh-
water lakes and reservoirs; anthropogenic gadolinium and geogenic
REEs in Lake Paranoá, Brasilia. C. R. Geoscience 347 (this issue).
Négrel, P., Petelet-Giraud, E., Guerrot, C., Millot, R., 2015. Lead isotopes
tracing weathering and atmospheric deposition in a small volcanic
catchment. C. R. Geoscience 347 (this issue).
Pédrot, M., Dia, A., Davranche, M., Martin, S., Al-Sid-Cheich, M., Gruau, G.,
2015. Unravelling the fate of arsenic during re-oxidation of reduced
wetland waters: experimental constraints and environmental conse-
quences. C. R. Geoscience 347 (this issue).
Petit, D., Véron, A., Flament, P., Deboudt, K., Poirier, A., 2015. Review of
pollutant lead decline in urban air and human blood: a case study
from north-western Europe. C. R. Geoscience 347 (this issue).
Stevenson, R., Poirier, A., Véron, A., Hillaire-Marcel, C., 2015. Late Eocene
to present isotopic Sr-Nd-Pb and geochemical evolution of sediments
from the Lomonosow Ridge, Arctic Ocean: implications for continen-
tal sources and linkage with North Atlantic Ocean. C. R. Geoscience
347 (this issue).
François Chabauxa,*, Jeroen E. Sonkeb, Philippe Négrelc,
Christophe Cloquetd
aLaboratoire d’hydrologie et de géochimie de Strasbourg,
EOST, Université de Strasbourg et CNRS, 1, rue Blessig,
67084 Strasbourg cedex, France
bLaboratoire Géosciences Environnement Toulouse,
Observatoire Midi-Pyrénées, CNRS/IRD/Université de
Toulouse, 14, avenue Édouard-Belin, 31400 Toulouse, France
cBRGM–LAB/DIR, avenue Claude-Guillemin,
45060 Orléans cedex 1, France
dService d’analyse des roches et des minéraux (SARM),
CRPG–CNRS, 15, rue Notre-Dame-des-Pauvres,
54501 Vandœuvre-lès-Nancy, France
*Corresponding author.
E-mail address: fchabaux@unistra.fr (F. Chabaux)
Available online 9 October 2015
Fig. 1. Jean Carignan (1965–2012).
1 M. Bau (Jacobs University, Bremen), M. Benedetti (Université Paris-
Diderot), V. Busigny (IPG, Paris), M. Chaussidon (CNRS, IPG, Paris),
C. Hissler (LIST, Luxembourg), S. Huon (UPMC, Paris), G. Imfeld (CNRS,
Strasbourg), C. Jeandel (CNRS, Toulouse), G. Leroux (CNRS, Toulouse), J.-
D. Meunier (CNRS, Aix-Marseille), S. Pichat (ENS Lyon), L. Reisberg (CNRS,
Nancy), T. Rigaudier (CRPG, Nancy), J. Sivry (Université Paris-Diderot),
P. Stille (CNRS, Strasbourg), D. Véron (CNRS, Aix-Marseille) are warmly
thanked for their help in reviewing the manuscripts of this thematic issue.


Using Pb isotopes in surface media to distinguish anthropogenic sources from undercover uranium sources

Archana Nair

Uma C. Mohanty

Tarini C. Panda

Handled by Didier Roche

Supplementary Material:

Improving the performance of precipitation outputs from Global Climate Models to predict monthly and seasonal rainfall over the Indian subcontinent


		Archana Nair1, U.C Mohanty1 and T.C.Panda2
			  archana.raman07@gmail.com


1School of Earth Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar
2 Odisha Engineering College, Cuttack, Bhubaneswar













S.1 Performance of Global Climate Models before any bias correction
        In this section, the performance of six GCMs at 357 gridpoints of Indian domain (10X10) is evaluated for each of the individual months of the monsoon and the season as a whole using correlation analysis. The correlation values between the GCM predicted precipitation and observed precipitation is found for a period of 1982-2013 and is shown in Figure S1 for JJAS season. The numbers on the upper right side of each subplot depicts the number of gridpoints which are found to have significant correlation (the significant correlation is greater than 0.3 by student’s t test at 90% confidence) with observed rainfall while the number on bottom right side of the subplot shows the value of correlation between observed rainfall and GCM simulated rainfall for the country as a whole. The subplot (a) to (f) illustrates different GCMs namely CCM3v6, CASST, CFSSST, CFSv2, COLA and GFDL. In the uncoupled model CCM3v6, only 47 gridpoints are found to have significant correlation and the model shows the correlation values widely spread over different parts of the country. The all India average correlation is found to be -0.2 which is not significant. The performance of CASST model is shown in subplot b. There are in all 35 gridpoints which are significantly correlated with observed rainfall and the major portion of significant correlation is found over northern parts of the country. Negative correlations are seen over most parts of the country and country as a whole depicts a correlation coefficient of 0.2. The next model (CFSSST) which is also an uncoupled model shows positive correlations over major portion of Northern India (Jammu & Kashmir, Punjab, Uttarakhand and parts of West Uttar Pradesh). The number of gridpoints with significant correlations is found to be only 28 and the all India average correlation is 0.1. The fourth model (CFSv2) which is a fully coupled model shows positive correlation over central parts of Indian subcontinent over Marathwada, Telangana, Coastal Andhra Pradesh, southern parts of Odisha and Konkan & Goa. There are 56 gridpoints where the correlation is found significant and the all India average correlation is found to be 0.5. The COLA model which is a coupled model shows 42 gridpoints which are significantly correlated with observed rainfall and the correlation is found to be 0.1 at all India basis. The next model is GFDL model which is also a coupled model and it shows significant correlation over most parts of the Central India. The country as a whole show a correlation value of 0.3 and the total number of significant gridpoints are 55. 
Similarly, this exercise is carried out for each of the individual months of monsoon for all the models. Due to brevity of study, we are only representing the performance of raw models in the month of July (Figure S2). Here also, the numbers of gridpoints that are found to be significant are shown at the upper right side of each subplot and the corresponding correlated values at all India level is shown at the bottom right side of the same plot. We can observe that in CCM3v6 model, the number of gridpoints having significant correlation and the all India level correlation value is higher than that observed for JJAS (Figure S2). The all India level correlation is found to be 0.1 and the number of gridpoints is 64. In the CASST model, the gridpoints that are significant is 51 and the all India average correlation is 0.4. Positive correlations are found over Northwestern parts of the country. The CFSSST model shows 49 gridpoints that are significant and the all India average correlation is 0.3. The CFSv2 model shows 56 gridpoints that has significant correlation with observed precipitation. The all India average correlation is found to be 0.5. The COLA model, on the other hand show 60 points that is found to be significant but the correlation value is 0.1 at all India level with positive correlations over Tamilnadu and Jammu & Kashmir. Large number of negatively correlated points is observed over most parts of the country and therefore the correlation for country as a whole is found to be very less. The GFDL model shows 51 points that are significant and the all India average correlation is found to be 0.3. 

S.2.Performance of model outputs after bias correction/synthetic superensemble (SSE) in the month of July

The CCM3v6 model after bias correction show significant improvement over its raw component. There are 71 gridpoints that are found to be significant and at all India level the correlation is 0.1. The CASST model show significant correlation in 83 gridpoints and at all India level the correlation is 0.4. In case of CFSSST model, the total numbers of significant gridpoints that are positively correlated are 80 and the all India average correlation is 0.3. In the CFSv2 model, the number of gridpoints that are positively correlated with observation increased to 92 from 56 but the correlation value at all India level is almost same. The COLA model also shows improvement over its biased counterpart. The numbers of gridpoints that are found to have significant correlation are 69 and the all India average correlation value is 0.1. In GFDL model 48 gridpoints are found to have significant correlation with observed rainfall and for country as a whole; a correlation value of 0.3 is seen.

S.3 Correlation Coefficient: EM vs SPCR
	The performance of EM of the GCMs is shown in Figure S4 for season as a whole and for June, July, August and September months (upper right: number of significant gridpoints; bottom right: all India correlation). In JJAS season, positive correlations are seen over Himachal Pradesh, Haryana, Uttarakhand, West Uttar Pradesh, East Rajasthan and parts of Odisha, Telangana, Tamilnadu, Rayalseema, West-Bengal & Sikkim and Nagaland-Mizoram-Manipur-Tripura (NMMT). The total numbers of gridpoints that are significant are 37 and country as a whole show a correlation of 0.3. In the month of June, parts of Jammu & Kashmir, Punjab, Himachal Pradesh, Uttarakhand, East Uttar Pradesh, Saurashtra & Kutch, Gujarat, Madhya Maharastra, South Interior Karnataka, Rayalseema, Telangana, Coastal Andhra Pradesh and Tamilnadu show positive correlation values. Negative correlations are seen over Assam & Meghayala, Bihar, West Rajasthan and Punjab. The number of gridpoints that are found to have significant correlation with observed rainfall is 43 and the value of correlation for country as a whole is 0.2. In the month of July, positive correlations are seen over Himachal Pradesh, Punjab, West Uttar Pradesh, Uttarakhand and East Uttar Pradesh. Positive correlations are also seen over North and South Interior Karnataka in the south peninsular part of the country. The number of gridpoints with positive correlation is 54 and the all India average correlation is 0.3. In the month of August, there are 59 gridpoints that are found to have positive correlation with observed rainfall and the all India average correlation is 0.5. Positive correlations are also seen over Northern India, Himachal Pradesh, Uttarakhand, Punjab, Haryana, West Rajasthan, Saurashtra& Kutch and Gujarat. Some patches of positive correlations are also found over South Interior Karnataka and Tamilnadu. In the month of September, positive correlations are seen over Jammu & Kashmir, Punjab, Himachal Pradesh, West Rajasthan and Haryana. Positive correlations are also seen over Northeast parts of the country over West-Bengal & Sikkim, Bihar, Andhra Pradesh and Assam & Meghalaya. Some parts of Madhya Maharashtra, North Interior Karnataka and Rayalseema also show positive correlations. 86 gridpoints are seen that are found to be significantly correlated with observed rainfall. The all India average correlation is found to be 0.6. 

S.3.1 Correlation Coefficient: EM vs SPCR

In this part of the study, the skill of SPCR technique is discussed for individual months of monsoon. In the month of June, the skill of SPCR over India as a whole is found to be 0.3. The number of gridpoints that are found to have significant correlation with observation is 41. One of the advancements of SPCR over EM is that negative correlations in EM are replaced by positive correlations. It can be seen that over parts of Jammu & Kashmir, Himachal Pradesh, Uttarakhand, West and East Uttar Pradesh, Vidharbha, Chhattisgarh, West Madhya Pradesh, Madhya Maharashtra positive correlations are seen which is absent in EM of the models. In the Northeast parts of the Indian subcontinent negative correlations which was seen in EM is improved and those points are now positively correlated with observed rainfall. In the month of July, the all India average correlation is found to be 0.4 and the number of gridpoints that are found significantly and positively correlated with observed rainfall is 104 which yielded an overall improvement of 15% of the gridpoints of the Indian domain. Positive correlation values are seen over Jammu & Kashmir, Punjab, Himachal Pradesh, West Rajasthan, Uttarakhand, Haryana, West Uttar Pradesh and parts of West and East Madhya Pradesh. Over Northwestern parts of the country (Saurashtra & Kutch and Gujarat) positive correlations are seen. The northern parts of Konkan & Goa also show positive correlation. Some positive correlations are also seen over Vidharbha and Marathwada subdivisions. In the month of August, positive correlations are found over Jammu & Kashmir and parts of South Interior Karnataka, Rayalseema, Tamilnadu. Positive correlations are also noticed over Marathwada, Odisha and Gangetic West Bengal. Over Northeast parts of the country Assam & Meghalaya and West Bengal & Sikkim positive correlations are seen. The all India average correlation is found to be 0.1 which is not significant and is much less than EM of the GCMs. This correlation is found to be less, as in this case, there are also gridpoints that are negatively correlated with observed rainfall and that is why the correlation is found to be less when the all India average is taken. The total numbers of gridpoints that are found significant with observed rainfall are 83. In the month of September, the number of gridboxes that are found to be positively correlated with observed rainfall is found to be 123 which show an overall improvement of 15% gridpoints more than the EM. The all India average correlation is found to be 0.7 which is also greater than EM of the models. Positive correlations are seen over northern India over Jammu & Kashmir, Himachal Pradesh, Punjab, Haryana, East Rajasthan, West Madhya Pradesh, Vidharbha, Chhatisgarh and Odisha. The Gujarat, Konkan & Goa, Madhya Maharashtra, Marathwada and Telangana also show positive correlation. The above discussion is also summarised in Table S1 where the performance of SPCR is shown over EM with the total number of gridpoints that are found significant and the all India level correlation value.

S.4 Predictive skill of SPCR in anomalous years

In the year 2012, the observed JJAS rainfall shows negative anomalies all over the Indian gridpoints. Some positive anomalies are seen over West Rajasthan, East Rajasthan, West Madhya Pradesh, parts of Jammu & Kashmir and northeast parts of the country. The positive anomalies over Odisha and Chhatisgarh are well represented in the EM of the models. Over rest of the country, negative anomalies are seen. In the SPCR technique positive anomalies are seen over West Rajasthan, East Rajasthan and parts of Jammu & Kashmir. The positive anomalies over Northeast are not represented well. In June, the observed rainfall shows negative anomalies all over the Indian subcontinent except Northeast of the country where positive anomalies are seen. In case of the EM, negative anomalies are seen over almost all parts of the country but negative anomalies over northeast parts of the country are not reproduced well. Positive anomalies are seen over Telangana, Chhatisgarh, Odisha, Vidharbha and East Madhya Pradesh. After applying the SPCR, the forecast for 2012 show negative anomalies all over the Indian gridpoints but the positive anomalies over Northeast are reproduced to some extent only. In July, positive anomalies are seen over West Madhya Pradesh, East Madhya Pradesh, Chhatisgarh, East Uttar Pradesh and parts of Jammu & Kashmir and Northeast. Over rest of the points, negative anomalies are seen. In case of the EM positive anomalies are seen over Northeast and parts of Odisha and Gangetic West Bengal. Over rest of the country, negative anomalies are seen. After SPCR, positive anomalies are found over Odisha, Gangetic West Bengal and over Northeast parts of the country. But the positive anomalies over other parts of the country are not captured well. Over rest of the country negative anomalies are seen. In the month of August, positive anomalies are seen over Jammu & Kashmir, West and East Rajasthan, Tamilnadu, over parts of Odisha, Chhatisgarh and Northeast parts of the country. Negative anomalies are seen over rest of the country. In the EM, negative anomalies are seen over most parts of the country and it is not able to capture the observed feature. After the SPCR technique, the positive anomalies are captured well over West and East Rajasthan, Tamilnadu, over parts of Odisha and Chhatisgarh. However, the positive anomalies over Northeast are not captured well. In the month of September, positive anomalies are seen over Saurashtra & Kutch, Gujarat, West and East Rajasthan, West Madhya Pradesh, Chhatisgarh, Odisha and parts of Northeast. In the EM, the models are not able to capture positive anomalies over Northwest parts of the country whereas over Odisha and part of Northeast the positive anomalies are captured well. In the case of SPCR, the positive anomalies over Northwestern parts of the country are well simulated beside the positive anomalies over Odisha and Chhatisgarh, though the magnitude is not well captured. Over Northeastern parts of the country, the positive anomalies are not seen. The negative anomalies over rest of the gridpoints are well captured.
















Table S1  Performance of the synthetic SPCR over the ensemble mean of the models
	EM MODEL	AFTER SPCR
JJAS	gridpoints	37	97
	correlation	0.3	0.4
JUNE	gridpoints	43	41
	correlation	0.2	0.3
JULY	gridpoints	54	104
	correlation	0.3	0.4
AUGUST	gridpoints	59	83
	correlation	0.5	0.1
SEPTEMBER	gridpoints	86	123
	correlation	0.6	0.7



Figure S1 The performance of raw GCMs shown at10 x 10  resolution for the JJAS season.








Figure S2 The performance of raw GCMs shown at10 x 10  resolution for July month







Figure S3 The performance of bias corrected GCMs shown at10 x 10  resolution in the month of July.








Figure S4 The performance of EM of the models for the season and individual months of monsoon.	




Figure S5 The rainfall anomalies shown for the year 2009 for EM (middle panel), for SPCR (right panel) and for observation (left panel). 


Figure S6 The rainfall anomalies shown for the year 2012 for EM (middle panel), for SPCR (right panel) and for observation (left panel). 



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