Politecnico di Milano

Scuola di Ingegneria Civile, Ambientale e Territoriale

Master of Science in

Civil, Environmental and Land Management Engineering

Index Based Analysis of Climate Change

Scenarios

Lake Como catchment case study

Supervisor:

Prof. Andrea F. Castelletti

Assistant Supervisor:

Dr. Yu Li

Master Graduation Thesis by:

Sui Xin

Student Id n. 822481

Academic Year 2014-2016

Attitude, basic skill and

psychological quality

------Lang Ping

ACKNOWLEDGMENTS

Firstly, I am thankful for my professor Andrea F. Castelletti gave me this chance to

join in my favorite project, which I should say, I feel like to join from the beginning

when I consider about my final thesis topic. And thanks my professor Andrea gave me

an opportunity to meet my Co-supervisor Yu Li. Yu is a very helpful person to give me

confidence to conquer every big difficulties during my thesis.

Then I’m really thankful for Politecnico di Milano, my mother campus. She constructs

my logical way of thought and grows me to be calm enough when I encounter every

task in life. That is a real value for a human.

Table of Contents

ACKNOWLEDGMENTS ................................................................................................................. 4

Abstract ........................................................................................................................................... 1

Sommario ........................................................................................................................................ 2

PREFACE ........................................................................................................................................ 3

1 Introduction ............................................................................................................................... 4

1.1 BACKGROUND .............................................................................................................................. 4

1.2 CLIMATE CHANGE SCENARIOS .......................................................................................................... 4

1.2.1 Representative Concentration Pathways (RCPs).................................................................. 6

1.2.2 Shared Socioeconomic Reference Pathways (SSPs). ............................................................ 9

1.2.3 General Circulation Models (GCMs) ................................................................................... 10

1.3 MOTIVATION ............................................................................................................................. 12

2 Study area ................................................................................................................................ 13

3 Materials and methods ............................................................................................................ 17

3.1 MATERIALS ................................................................................................................................ 17

3.1.1 Observational dataset ....................................................................................................... 17

3.1.2 Climate projection dataset ................................................................................................ 18

3.2 METHODS ................................................................................................................................. 19

3.2.1 Climate extreme Index ....................................................................................................... 19

4 Results ..................................................................................................................................... 25

4.1 TRENDS IN ANNUAL TEMPERATURE INDICES ............................................................................................. 25

4.2 TRENDS IN ANNUAL PRECIPITATION INDICES ............................................................................................ 34

4.2.1 Precipitation in annual precipitation indices ..................................................................... 34

4.2.2 Trends in seasonal precipitation indices ............................................................................ 34

5.Discussion ................................................................................................................................. 40

6.Conclusion ................................................................................................................................. 43

List of Figures .............................................................................................................................. 44

List of Tables ................................................................................................................................ 45

Acronyms ...................................................................................................................................... 45

Bibliography ................................................................................................................................. 47

1

ABSTRACT

Recent climate extreme events demonstrate the vulnerability of European society to

climate-related natural hazards, and there is a strong evidence that climate change

will worsen these events in the coming years. In other word, future climate extremes

may be very different from today and difficult to predict.

To this end, this thesis conducted an in-depth analysis of future climate scenarios with

a set of state-of-the-art indices. The analysis is based on the projected climate

change time-series under Representative Concentration Pathways (RCP) 2.6

scenario, generated from a number of Global Climate Models (GCMs). The dataset is

based on daily max and min temperature, as well as daily precipitation. Lake Como

catchment (Italy) is used as the study area to investigate projected changes of the

climatic conditions till the end of this century. 27 key indices are computed to depict

the future extreme situations (such as extreme dry periods or heavy precipitation),

based on which the impacts of climate change to the human society will be interpreted.

Those results are also useful for different stakeholders, such as several hydropower

companies and agricultural authorities, to identify the critical components from the

changing climate conditions and to plan for strategic mitigation measures accordingly.

2

SOMMARIO

Clima recenti eventi estremi dimostrano la vulnerabilità della società europea per i

pericoli naturali connesse al clima, e vi è una forte evidenza che il cambiamento

climatico peggiorerà questi eventi nei prossimi anni. In altre parole,

futuri eventi climatici estremi possono essere molto diverse da oggi e difficili da

prevedere.

A tal fine, questa tesi ha condotto un'analisi approfondita degli scenari climatici futuri,

con una serie di indice di state-of-the-art. L'analisi si basa sul cambiamento climatico

serie temporale proiettata sotto Pathways rappresentativi di concentrazione (RCP)

2.6 scenario, generato da un certo numero di modelli globali climatici (GCM). Il set di

dati si basa sulla massima giornaliera e temperature min, così come precipitazioni

giornaliere. Lake Como utenza (Italia) viene utilizzato come area di studio per

analizzare cambiamenti previsti delle condizioni climatiche fino alla fine di questo

secolo. 27 indice chiave sono calcolati per rappresentare le situazioni future estreme

(come ad esempio i periodi di siccità estreme o forti precipitazioni), in base ai quali

verranno interpretati gli impatti dei cambiamenti climatici per la società umana. Questi

risultati sono utili anche per le diverse parti interessate, come le diverse aziende

idroelettriche e le autorità agricole, per identificare i componenti critici dalle condizioni

climatiche mutevoli e per pianificare misure strategiche di mitigazione di

conseguenza.

3

PREFACE

Being an environmental engineering student for nearly 7 years, I have a really

ardently love for the natural and the environment. As the Global Warming becomes

more severe, the impact as occurred around my life can be clearly perceived. Before

in the winter, snow in Barzio mountain could last for nearly four months. However, in

recent years the period is shorten to 3 months, since snow begin to melt in early

spring. Consequently, flood problem happens more frequently in autumn in Como city,

which makes me wonder how the future will be and what method can we utilize to

have a better predict our future.

Natural Resource Management subject is my favorite study area during my master

period. This subject takes the environmental factors and also humans into account,

and aims to support people in better manage the environment in a friendlier and

sustainable way.

4

1 INTRODUCTION

1.1 BACKGROUND

The research on the climate change extremes has progressed enormously, largely

due to international coordinated efforts to simulate the future climate projections with

collated, quality controlled climate models, which serve as a basis to analyze

parameters of interests and associated extreme events that may occur in the future.

One such effort has been led by CORDEX project (Giorgi F et al. 2015), who have

facilitated the dissemination of climate change dataset over global scale with various

parameters available. Further effort has been made through the provision of free

standardized software for analysis of extreme events, and through the organization of

regional workshops to fill in data gap in data-spare regions (Peterson and Manton,

2008). For the example of such standardized software, just to name a few, CLIMDEX

project was set up to produce a suite of in situ and gridded land-based global datasets

of indices representing the more extreme aspects of climate. The package contains

27 indices recommended by ETCCDI (Zhang et al. 2011). Another example is the

HadDEX dataset, which currently represents the most comprehensive global gridded

dataset of temperature and precipitation extremes based daily in situ data available. It

has been used in many model evaluation (e.g., Sillmann and Roekner 2008;

Alexander and Arblaster, 2009; Rusticucci et al., 2010; Sillmann et al., 2012) and

detection and attribution studies (e.g., Min et al. 2011; Morak et al. 2011), in addition

to climate variability and trend studies.

1.2 CLIMATE CHANGE SCENARIOS

Depicting the plausible picture of changing conditions in the future is critical for

understanding the consequence of climate change and for planning the adaptation

strategies accordingly. To this end, projection of climate change scenarios via

integrated model simulation can serve as a useful basis to facilitate the

abovementioned processes. This task has been worked many years by the

intergovernmental panel on climate change (IPCC), which is an international body for

assessing the science related to climate change. The IPCC was set up in 1988 by the

world meteorological organization (WMO) and United Nations environment program

(UNEP) to provide policymakers with regular assessments of the scientific basis of

climate change, its impacts and future risks, and options for adaptation and mitigation.

IPCC assessments provide a scientific basis for governments at all levels to develop

climate-related policies, and they underlie negotiations at the UN climate conference -

the United Nations Framework Convention on Climate Change (UNFCCC). The

assessments are policy-relevant but not policy prescriptive.

5

It is worth to mention that one of the main commissions of IPCC is to approve sets of

scenarios for climate research, as well as to provide guidance for producing those

scenarios. Previous scenario exercises in the climate change research community

were developed and applied sequentially in a linear causal chain that extended from

the socioeconomic factors that influence greenhouse gas emissions to atmospheric

and climate processes to impacts (see Figure 1-1). Detailed socioeconomic scenarios

were developed first and used to prepare emissions scenarios, which in turn were

used in climate model experiments that formed the basis of climate change

projections. Lags in the development process meant that it was often many years until

climate and socioeconomic scenarios were available for use in studies of impacts,

adaptation, and vulnerability. Basing the scenarios first on socioeconomic

characteristics also led to sets that did not necessarily fully span the literature range

on future emissions and climate response.

In the new process recommended latest fifth report, emissions and socioeconomic

scenarios are developed in parallel, building on different trajectories of radiative

forcing over time. Rather than starting with detailed socioeconomic storylines to

generate emissions and then climate scenarios, the new process begins with a limited

number of alternative pathways (trajectories over time) of radiative forcing levels (or

CO2 equivalent concentrations) that are both representative of the emissions scenario

literature and span a wide space of resulting greenhouse gas concentrations that lead

to clearly distinguishable climate futures. These radiative forcing trajectories were

thus termed “Representative Concentration Pathways” (RCPs). The RCPs are not

associated with unique socioeconomic assumptions or emissions scenarios but can

result from different combinations of economic, technological, demographic, policy,

and institutional futures. In the preparatory phase, each RCP was simulated in an

Integrated Assessment model to provide one internally consistent plausible pathway

of emissions and land use change that leads to the specific radiative forcing target.

The full set of RCPs spans the complete range of integrated assessment literature on

emissions pathways and the radiative forcing targets are distinct enough to result in

clearly different climate signals.

6

Figure 1-1. Approaches to the development of global scenarios: (a) previous sequential approach ; (b)

proposed parallel approach. Numbers indicate analytical steps (2a and 2b proceed concurrently). Arrows

indicate transfers of information (solid) selection of RCPs.

1.2.1 Representative Concentration Pathways (RCPs)

RCP are four greenhouse gas concentration trajectories adopted by IPCC for its fifth

assessment report in 2014. It supersedes Special Report on Emissions Scenarios

(SRES) projections published in 2000 (Nakicenovic et al. 2000).

The pathways are used for climate modeling and research. They describe four

possible climate futures, all of which are considered possible depending on how much

greenhouse gases are emitted in the years to come. The four RCPs, namely RCP 2.6,

RCP 4.5, RCP 6, and RCP 8.5, are named after a possible range of radiative forcing

values in the year 2100 relative to pre-industrial values (i.e., + 2.6, + 4.5, + 6.0, and +

8.5 W/m2, respectively; See Table 1-1).

7

Table 1-1. Overview of four RCPs

Description

RCP 2.6 Rising radiative forcing pathway leading to 2.6 W/m² in 2100.

RCP 4.5 Stabilization without overshoot pathway to 4.5 W/m² at

stabilization after 2100

RCP 6 Stabilization without overshoot pathway to 6W/m2 at

stabilization after 2100

RCP 8.5 Peak in radiative forcing at 8.5 W/m² before 2100 and decline

The RCPs are consistent with a wide range of possible changes in future

anthropogenic (i.e., human) GHG emissions. RCP2.6 assumes that global annual

GHG emissions (measured in CO2-equivalents) peak between 2010-2020, with

emissions declining substantially thereafter. Emissions in RCP4.5 peak around 2040,

then decline. In RCP6, emissions peak around 2080, then decline. In RCP8,5,

emissions continue to rise throughout the 21st century.

The population and GDP pathways underlying the four RCPs are shown in Figure 1-2.

It also shows, as reference, the UN population projections and the 90th percentile

range of GDP scenarios in the literature on greenhouse gas emission scenarios. It

should be noted that, with one exception (RCP8.5), the modeling teams deliberately

made intermediate assumptions about the main driving forces (as illustrated by their

position in Figure 1-2) (see the relevant papers elsewhere in this Special Issue). In

contrast, the RCP8.5 was based on a revised version of the SRES A2 scenario; here,

the storyline emphasizes high population growth and lower incomes in developing

countries.

8

Figure 1-2. Population and GDP projections of the four scenarios underlying the RCPs. Grey area for

population indicates the range of the UN scenarios (low and high) (UN 2003). Grey area for income

indicates the 98th and 90th percentiles (light/dark grey) of the IPCC AR4 database (Hanaoka et al. 2006).

The dotted lines indicate four of the SRES marker scenarios.

For energy use, the scenarios underlying the RCPs are consistent with the literature -

with the RCP2.6, RCP4.5 and RCP6 again being representative of intermediate

scenarios in the literature (resulting in a primary energy use of 750 to 900 EJ in 2100,

or about double the level of today; see Figure 1-3). The RCP8.5, in contrast, is a

highly energy-intensive scenario as a result of high population growth and a lower rate

of technology development. In terms of the mix of energy carriers, there is a clear

distinction across the RCPs given the influence of the climate target (for details, see

the papers elsewhere in this Special Issue). Total fossil-fuel use basically follows the

radiative forcing level of the scenarios; however, due to the use of carbon capture and

storage (CCS) technologies (in particular in the power sector), all scenarios, by 2100,

still use a greater amount of coal and/or natural gas than in the year 2000. The use of

oil stays fairly constant in most scenarios, but declines in the RCP2.6 (as a result of

depletion and climate policy). The use of non-fossil fuels increases in all scenarios,

especially renewable resources (e.g. wind, solar), bio-energy and nuclear power. The

main driving forces are increasing energy demand, rising fossil-fuel prices and climate

policy. An important element of the RCP2.6 is the use of bio-energy and CCS,

resulting in negative emissions (and allowing some fossil fuel without CCS by the end

of the century).

9

Figure 1-3. Development of primary energy consumption (direct equivalent) and oil consumption for the

different RCPs. The grey area indicates the 98th and 90th percentiles (light/dark grey) (AR4 database

(Hanaoka et al. 2006) and more recent literature (Clarke et al. 2010; Edenhofer et al. 2010)). The dotted

lines indicate four of the SRES marker scenarios.

1.2.2 Shared Socioeconomic Reference Pathways (SSPs).

The SSPs define the state of human and natural societies at a macro scale and have

two elements: a narrative story line and a set of quantified measures that define the

high-level state of society as it evolves over the 21-st century under the assumption of

no significant climate feedback on the SSP. This assumption allows the SSP to be

formulated independently of a climate change projection. In reality, SSPs may be

affected by climate change, which can be taken into account when combining SSPs

with climate change projections to generate a socioeconomic-climate reference

scenario. In the absence of climate policies, the SSPs may lead to different climate

forcing in the reference case and to different changes in climate.

In Figure 1-4 representation, two axes of the scenario matrix are the SSPs and

radiative forcing levels. Each combination of an SSP and a radiative forcing level

defines a family of macro-scale scenarios. Because the RCP level provides only a

rudimentary specification of mitigation policy characteristics, and very little information

on adaptation policies, a third axis embeds RCPs in Shared Climate Policy

Assumptions (SPAs) that include additional information on mitigation and adaptation

policies, e.g. global and sectoral coverage of greenhouse gas reduction regimes, and

the aggressiveness of adaptation in different world regions. Obviously, there can be

more than one SPA for a given radiative forcing level. For any combination of SSP,

RCP, and SPA, there will be a number of possible climate change projections that are

associated with a different model of the physical climate system, adding another

dimension to each cell.

10

Figure 1-4. The scenario matrix architecture: confronting different future levels of climate forcing with

different socio-economic reference assumptions described by SSPs.

1.2.3 General Circulation Models (GCMs)

GCMs, which represent physical processes in the atmosphere, ocean, cryosphere

and land surface, are the most advanced tools currently available for simulating the

response of global climate system to increasing greenhouse gas concentrations.

While simpler models have also been used to provide globally or regionally averaged

estimates of the climate response, only GCMs, possibly in conjunction with nested

regional models, have the potential to provide geographically and physically

consistent estimates of regional climate change which are required in impact analysis.

The results from GCMs are a set of global dataset that describes future changes of

climate forcing under different socio-economic and RCP scenarios. For example,

Figure 1-5 shows projected changes worldwide on a regional level in response to

different scenarios of increasing carbon dioxide simulated by 21 climate models, and

are used to help scientists and planners conduct climate risk assessments to better

understand local and global effects of hazards, such as severe drought, floods, heat

waves and losses in agriculture productivity.

11

Figure 1-5. Coupled Model Inter comparison Project Phase 5 (CMIP5) multi-model mean projections(i.e.,

the average of the model projections available) for the 2081–2100 period under the RCP2.6 (left) and

RCP8.5 (right) scenarios for (a) change in annual mean surface temperature and (b) change in annual

mean precipitation, in percentages, and (c) change in average sea level. Changes are shown relative to

the 1986–2005 period. The number of CMIP5 models used to calculate the multi-model mean is

indicated in the upper right corner of each panel. Stippling (dots) on (a) and (b) indicates regions where

the projected change is large compared to natural internal variability (i.e., greater than two standard

deviations of internal variability in 20-year means) and where 90% of the models agree on the sign of

change. Hatching (diagonal lines) on (a) and (b) shows regions where the projected change is less than

one standard deviation of natural internal variability in 20-year means

Yet, in using those dataset one has to tack with spatial resolution of the output from

GCMs, which are often too coarse relative to the scale of exposure units in most

impact assessment, hence as those processes related to smaller scales might not be

properly captured. Instead, their known properties must be averaged over the larger

scale in a technique known as parameterization. Other alternatives are also available

to mitigate this issue by using downscaling approaches via regional modeling or

statistical methods (Sylwia Trzaska, 2014).

12

1.3 MOTIVATION

The purpose of the current study is to analyze the projected climate change time

series using different indices, which may allow to uncover the multi-dimensional

facets from climate change dataset. This should allow us to explore the potential

stressors beneath the general trend of increasing temperature as reported by many

studies.

13

2 STUDY AREA

Figure 2-1. Map of Lombardy region (Italy). The study area, i.e., Lake Como catchment, is colored in

violet located in northern part.

Lake Como is the third largest Italian lake located in northern Italy close to

Switzerland (see Figure 2-1), and receives water from a catchment of around 4,500

km2 characterized by a highly varying terrain elevation, which provides a huge

hydro-power potential exploited through a series of small to medium artificial

reservoirs for a total storage capacity of 545 Mm3 (green triangles in Figure 2-1).

Since 1946, Lake Como is regulated by Consorzio dell’Adda1, a consortium of

downstream stakeholders mostly composed by farmers but also hydro-power

companies and industries. Lake Como has an active storage of around 250 Mm3

14

regulated through a dam on the effluent Adda river and is operated for multiple

purposes, including a number of run-of-river hydro-electric power plants and several

large agricultural districts (green area downstream shown in Figure 2-1). Besides

water supply, the regulation of the lake aims urban flood protection of the lake shores,

particularly in Como City. The hydrological regime is influenced by both spring

snowmelt and precipitation, resulting into a bi-modal peak (see Figure 2-2 green and

blue plots): one more pronounced peak corresponding to the snow-melt season, in

late spring, and a smaller but more variable one produced by autumn rains. The

spring peak (from May to July) is the most important contribution to the seasonal

storage, which is released during the summer, when the agricultural water demand is

high (violet plot in Figure 2-2). While water availability has not been an issue for many

years, in recent decade more drought events have been reported. Figure 2-3

visualizes the trend in the inflows observed over the last 60 years using a tool called

Moving Average over Shifting Horizon (MASH; Anghileri, Pianosi, and Soncini-Sessa

2014), a trend analysis technique that aims to identify non-stationary changes in

hydro-climatic variables. As shown in Figure 2-3, there is a clear decreasing trend of

inflow during the late spring and summer periods, which are the most critical for

irrigated agriculture. In fact, in 2003, 2005, 2006 and 2007 the system experienced

severe drought events, which caused great losses to the agriculture (Anghileri,

Pianosi, and Soncini-Sessa, 2014). If this tendency continues over next years, the

system is likely to lose its designed functionality, and adoption of adaptation strategies

will be indispensable.

15

Figure 2-2. Hydrological features of Lake Como estimated as the mean of statistics between 2006 - 2013,

and the nominal water demand trajectory is given by historical regulation policy. Notice that the natural

storage estimates are obtained by regression method assuming no regulation imposed.

16

Figure 2-3. Trend analysis of the daily inflows over the time horizon 1946-2010, with colors of each line

representing the average extra-annual inflow series from every 20-year moving average estimates. The

average daily inflow is computed by means of a moving window that includes data over consecutive days

in the same year and over the same days in consecutive years, with the horizon of consecutive years

progressively shifted ahead to identify long-term trends.

Recent hydrological extreme events demonstrate the vulnerability of European

society to water-related natural hazards, and there is strong evidence that climate

change will worsen these events in the coming years. Future hydrological extremes

may be very different from today’s reality and difficult to predict. Changed

hydro-climatic extremes will have important implications on the water sector and the

design of water management practices. Therefore, there is an urgent need to better

understand the changing conditions projected in the future, and the potential stressors

that associate with such changing conditions.

17

3 MATERIALS AND METHODS

3.1 MATERIALS

3.1.1 Observational dataset

E-OBS gridded version of the ECA dataset with daily temperature and precipitation is

used as ground truth observation. The ECA dataset contains series of daily

observations at meteorological stations throughout Europe and the Mediterranean.

The data files contain gridded data for 4 variables (daily mean temperature, daily

minimum temperature, daily maximum temperature and daily total precipitation).

Table 3-1 summarizes the main feature of the EOBS dataset, and Figure 3-1 shows

the domain of our study area within the EOBS grids.

Table 3-1. Feature of EOBS observatory dataset

Description

Time span 1/1/1950 – 12/31/2015

Spatial resolution 0.25 by 0.25 degree

Temporal resolution daily

Figure 3-1. Map of EOBS grid dataset for the study area

18

3.1.2 Climate projection dataset

The World Climate Research Program (WCRP) established in 2009 the Task Force

for Regional Climate Downscaling (TFRCD), Which created the CORDEX initiative to

generate regional climate change projections for all terrestrial regions of the globe

within the timeline of the Fifth Assessment Report (AR5) and beyond. The major aims

of the CORDEX initiative are to provide a coordinated model evaluation framework, a

climate projection framework, and an interface to the applicants of the climate

simulations in climate change impact, adaption, and mitigation studies (Giorgi et al.,

2009).

The EURO-CORDEX is a branch of CORDEX initiative that produces ensemble

climate simulations based on multiple dynamical and empirical-statistical downscaling

models forced by multiple global climate models from Coupled Model Inter

Comparison Project Phase 5 (CMIP5). Simulations of EURO-CORDEX consider the

global climate simulations from the CMIP5 long term experiments up to the year 2100.

They are based on greenhouse gas emission scenarios (i.e., RCPs) corresponding to

stabilization of radiative forcing after the 21st century (Moss et al., 2010 and 2008;

Nakicenovic et al., 2000; Van Vuuren et al., 2008).

In this study, we adopted the climate projection dataset RCP2.6 radiative scenarios,

meaning the optimal situation assuming GHG emissions peak between 2010-2020

and decline substantially thereafter. Table 1-1 summarizes the main feature of the

EOBS dataset, and Figure 3-2 shows the domain of our study area within the EOBS

grids.

Table 3-2. Features of EURO-CORDEX dataset

Description

Time span 2006 - 2100 for projection

1950 - 2005 for control period

Spatial resolution 0.44 by 0.44 degree

Temporal resolution daily

19

Figure 3-2. The map of study area within EURO-CORDEX grid domain.

3.2 METHODS

3.2.1 Climate extreme Index

To monitor changes in climate and climate extremes, a set of key indices has been

computed (e.g. Frich et al., 2001). A good index is expected to have a clear meaning,

be highly relevant to people, provide insights into climate change, be homogeneous,

easy to understand, be relevant to the practical concerns of policy makers and should

not smooth out potentially important changes. Guided by those criteria, a set of 27

indices indicative of climate change are structured based on CLIMDEX project. A

detail definition on each index is provided below:

Those index decompose the projection of climate change time-series into various

aspects representing the extreme events. Indices are derived from daily temperature

and precipitation data using the definitions recommended by the expert team on

Climate Change Detection and Indices (ETCCDI).

1. FD. Number of frost days: Annual count of days when TN (daily minimum

temperature) < 0. Let TNij be daily minimum temperature on day i in year j.

Count the number of days where: TNij < 0.

20

2. SU. Number of summer days: Annual count of days when TX (daily maximum

temperature) > 25. Let TXij be daily maximum temperature on day i in year j.

Count the number of days where: TXij > 25.

3. ID. Number of icing days: Annual count of days when TX (daily maximum

temperature) < 0. Let TXij be daily maximum temperature on day i in year j.

Count the number of days where: TXij < 0.

4. TR. Number of tropical nights: Annual count of days when TN (daily minimum

temperature) > 20. Let TNijbe daily minimum temperature on day i in year j.

Count the number of days where: TNij > 20.

5. GSL. Growing season length: Annual (1st Jan to 31st Dec in Northern

Hemisphere (NH), 1st July to 30th June in Southern Hemisphere (SH)) count

between first span of at least 6 days with daily mean temperature TG>5 and

first span after July 1st (Jan 1st in SH) of 6 days with TG<5. Let TGij be

daily mean temperature on day i in year j. Count the number of days between

the first occurrence of at least 6 consecutive days with: TGij > 5. And the

first occurrence after 1st July (1st Jan. in SH) of at least 6 consecutive days

with: TGij < 5.

6. TXx. Monthly maximum value of daily maximum temperature: Let TXx be the

daily maximum temperatures in month k, period j. The maximum daily

maximum temperature each month is then: TXxkj=max(TXxkj)

7. TNx. Monthly maximum value of daily minimum temperature: Let TNx be the

daily minimum temperatures in month k, period j. The maximum daily

minimum temperature each month is then: TNxkj=max(TNxkj)

8. TXn. Monthly minimum value of daily maximum temperature: Let TXn be the

daily maximum temperatures in month k, period j. The minimum daily

maximum temperature each month is then: TXnkj=min(TXnkj)

9. TNn. Monthly minimum value of daily minimum temperature: Let TNn be the

daily minimum temperatures in month k, period j. The minimum daily minimum

temperature each month is then: TNnkj=min(TNnkj)

10. TN10p. Percentage of days when TN < 10th percentile: Let TNij be the daily

minimum temperature on day i in period j and let TNin10 be the calendar day

10th percentile centered on a 5-day window for the base period 1961-1990.

The percentage of time for the base period is determined where: TNij <

TNin10. To avoid possible inhomogeneity across the in-base and out-base

periods, the calculation for the base period (1961-1990) requires the use of a

bootstrap processes. Details are described in Zhang et al. (2005) .

11. TX10p. Percentage of days when TX < 10th percentile: Let TXij be the daily

maximum temperature on day i in period j and let TXin10 be the calendar day

10th percentile centered on a 5-day window for the base period 1961-1990.

The percentage of time for the base period is determined where: TXij <

TXin10.To avoid possible inhomogeneity across the in-base and out-base

21

periods, the calculation for the base period (1961-1990) requires the use of a

bootstrap processes. Details are described in Zhang et al. (2005) .

12. TN90p. Percentage of days when TN > 90th percentile: Let TNij be the daily

minimum temperature on day i in period j and let TNin90 be the calendar day

90th percentile centered on a 5-day window for the base period 1961-1990.

The percentage of time for the base period is determined where: TNij >

TNin90. To avoid possible inhomogeneity across the in-base and out-base

periods, the calculation for the base period (1961-1990) requires the use of a

bootstrap procedure. Details are described in Zhang et al. (2005) .

13. TX90p. Percentage of days when TX > 90th percentile: Let TXij be the daily

maximum temperature on day i in period j and let TXin90 be the calendar day

90th percentile centered on a 5-day window for the base period 1961-1990.

The percentage of time for the base period is determined where: TXij > TXin90.

To avoid possible inhomogeneity across the in-base and out-base periods, the

calculation for the base period (1961-1990) requires the use of a bootstrap

procedure. Details are described in Zhang et al. (2005) .

14. WSDI. Warm spell duration index: Annual count of days with at least 6

consecutive days when TX > 90th percentile. Let TXij be the daily maximum

temperature on day i in period j and let TXin90 be the calendar day

90th percentile centered on a 5-day window for the base period 1961-1990.

Then the number of days per period is summed where, in intervals of at least 6

consecutive days: TXij > TXin90

15. CSDI. Cold spell duration index: Annual count of days with at least 6

consecutive days when TN < 10th percentile. Let TNij be the daily maximum

temperature on day i in period j and let TNin10 be the calendar day

10th percentile centered on a 5-day window for the base period 1961-1990.

Then the number of days per period is summed where, in intervals of at least 6

consecutive days: TNij < TNin10

16. DTR. Daily temperature range: Monthly mean difference between TX and TN.

Let TXij and TNij be the daily maximum and minimum temperature

respectively on day i in period j. If I represent the number of days in j, then:

I

TnTx

DTR

I

i

ijij

1j

)(

17. Rx1day. Monthly maximum 1-day precipitation: Let RRij be the daily

precipitation amount on day i in period j. The maximum 1-day value for

period j are: Rx1dayj = max (RRij)

18. Rx5day. Monthly maximum consecutive 5-day precipitation: Let RRkj be the

precipitation amount for the 5-day interval ending k, period j. Then maximum

5-day values for period j are: Rx5dayj = max (RRkj)

22

19. SDII. Simple precipitation intensity index: Let RRwj be the daily precipitation

amount on wet days, w (RR ≥ 1mm) in period j. If W represents number of wet

days in j, then:

W

RR

SDII

W

wj 1w

j

)(

20. R10mm. Annual count of days when PRCP≥ 10mm: Let RRij be the daily

precipitation amount on day i in period j. Count the number of days where:

RRij ≥ 10mm

21. R20mm. Annual count of days when PRCP≥ 20mm: Let RRij be the daily

precipitation amount on day i in period j. Count the number of days where:

RRij ≥ 20mm

22. Rnnmm. Annual count of days when PRCP≥nnmm, nn is a user defined

threshold: Let RRij be the daily precipitation amount on day i in period j. Count

the number of days where: RRij ≥ nnmm

23. CDD. Maximum length of dry spell, maximum number of consecutive days

with RR < 1mm: Let RRij be the daily precipitation amount on day iin period j.

Count the largest number of consecutive days where: RRij < 1mm

24. CWD. Maximum length of wet spell, maximum number of consecutive days

with RR ≥ 1mm: Let RRij be the daily precipitation amount on day iin period j.

Count the largest number of consecutive days where: RRij ≥ 1mm

25. R95pTOT. Annual total PRCP when RR > 95p. Let RRwj be the daily

precipitation amount on a wet day w (RR ≥ 1.0mm) in period i and

letRRwn95 be the 95th percentile of precipitation on wet days in the

1961-1990 period. If W represents the number of wet days in the period, then:

W

w

wjj RRPR1

95 where RR 95wj wnRRRR

26. R99pTOT. Annual total PRCP when RR > 99p: Let RRwj be the daily

precipitation amount on a wet day w (RR ≥ 1.0mm) in period i and

letRRwn99 be the 99th percentile of precipitation on wet days in the

1961-1990 period. If W represents the number of wet days in the period, then:

W

w

wjj RRPR1

99 where RR 99wj wnRRRR

27. PRCPTOT. Annual total precipitation in wet days: Let RRij be the daily

precipitation amount on day i in period j. If I represents the number of days in j,

then

I

i

ijRRPRCPTOT1

j

23

Those indices can be divided into 5 different categories:

1. Percentile-based indices include occurrence of cold nights (TN10p),

occurrence of warm nights (TN90p), occurrence of cold days (TX10p),

occurrence of warm days (TX90p), very wet days (R95p) and extremely wet

days (R99p). The temperature percentile-based indices sample the coldest

and warmest deciles for both maximum and minimum temperatures, enabling

us to evaluate the extent to which extremes are changing. The precipitation

indices in this category represent the amount of rainfall falling above the 95th

(R95p) and 99th (R99p) percentiles and include, but are not be limited to, the

most extreme precipitation events in a year.

2. Absolute indices represent maximum or minimum values within a season or

year. They include maximum daily maximum temperature (TXx), maximum

daily mini-mum temperature (TNx), minimum daily maximum temperature

(TXn), minimum daily minimum temperature (TNn), maximum 1-day

precipitation amount (RX1day) and maximum 5-day precipitation amount

(RX5day).

3. Threshold indices are defined as the number of days on which a temperature

or precipitation value falls above or below a fixed threshold, including annual

occurrence of frost days (FD), annual occurrence of ice days (ID), annual

occurrence of summer days (SU), annual occurrence of tropical nights (TR),

number of heavy precipitation days > 10 mm (R10) and number of very heavy

precipitation days > 20 mm (R20). These indices are not necessarily

meaningful for all climates because the fixed thresholds used in the definitions

may not be applicable everywhere on the globe. However, previous studies

(e.g., Frich et al., 2002; Kiktev et al., 2003) have shown that temperature

indices such as FD, the number of days on which minimum temperature falls

below 0LC, have exhibited coherent trends over the mid latitudes during the

second half of the 20th century. In addition, changes in these indices can have

profound impacts on particular sectors of society or eco-systems. So we

included the indices in our study, even though some of them may not provide

truly “global” spatial coverage or be truly extreme.

4. Duration indices define periods of excessive warmth, cold, wetness or dryness

or in the case of growing season length, periods of mildness. They include

cold spell duration indicator (CSDI), warm spell duration indicator (WSDI),

growing season length (GSL), consecutive dry days (CDD) and consecutive

wet days (CWD). Many of these indices were used in the near global analysis

of Frich et al. (2002). The heat wave duration index (HWDI) defined by Frich et

al. (2002) has been found not to be statistically robust as it had a tendency to

have too many zero values (Kiktev et al., 2003). This is because Frich et al.

(2002) used a fixed threshold to compute the index. This threshold is too high

in many regions, such as the tropics, where the variability of daily temperature

24

is low. To overcome this, the ETCCDMI replaced this index with the warm

spell duration index (WSDI) which is calculated using a percentile based

threshold. As this index only sampled daytime maxima we also chose to

analyze spells of nighttime minima (CSDI). The CDD index is the length of the

longest dry spell in a year while the CWD index is defined as the longest wet

spell in a year. This category of indices also includes the length of the growing

season (GSL) which is an index that is generally only meaningful in the

Northern Hemisphere extra tropics.

Other indices include indices of annual precipitation total (PRCPTOT), diurnal

temperature range (DTR), simple daily intensity index (SDII), extreme temperature

range (ETR) and annual contribution from very wet days (R95pT). They do not fall into

any of the above categories but changes in them could have significant societal

impacts.

All of the climate indices can be calculated using precipitation, maximum temperature,

and minimum temperature dataset. Those indices were chosen primarily for

assessment of the many aspects of a changing global climate which include changes

in intensity, frequency and duration of temperature and precipitation events. They

represent events that occur several times per season or year giving them more robust

statistical properties than measures of extremes which are far enough into the tails of

the distribution so as not to be observed during some years. Together they enabled

the presentation of an up-to-date and comprehensive picture of trends in extreme

related to temperature and precipitation changes.

Before initializing the computation those index, a few steps are required to preprocess

the observation and climate change projection dataset. The workflow of

preprocessing of required inputs can be described below:

1. Quality control of the data to fill the missing value and to construct a complete

time series.

2. Aggregation of spatial distributed dataset over Lake Como basin using

Thiessen polygon approach.

3. Bias-correction and downscaling of climate projection data using quantile

mapping approach.

Once the final dataset is prepared, the 27 climate change indices were calculated.

25

4 RESULTS

4.1 TRENDS IN ANNUAL TEMPERATURE INDICES

All temperature-related indices show significant and widespread warming trends,

which are generally stronger for indices calculated from daily minimum (night time)

temperature than for those calculated from daily maximum (daytime) temperature.

For example, the frequency of cool nights based on daily minimum temperatures is

shown to have significant decreased almost everywhere during this century (see

Figure 4-5). The strongest reductions, up to 15 days between 2005 and this century

period. Correspondingly, at the upper tail of the minimum temperature of warm nights

in almost all seasons (Figure 4-5). Globally average, the frequency of warm nights

has increased by about 55% (20 days in a year) during the 100 years’ time series.

98% show significant increases in TN90p and decreases in TN10p, respectively.

Mostly warming trends are also apparent in the absolute warmest and coldest

temperature of the year. The warming is generally stronger for the coldest than for the

warmest value. Since the middle of the 21st century the coldest night (See Figure

4-16) and coldest day (see Figure 4-14) of the year, for example, have significantly

increased over Como region. Warming trends are particularly strong (up to 1 ) over

Como lake region. The result also shows significant increases in TNn (TXn) during

2005-2100 period, whereas significant decreases are only found in temperature

related to the coldest night of year (TNn) has increased by about 4 in the time

series.

26

Figure 4-1. Change in the number of frost days.

Figure 4-2. Change in the number of summer days.

27

Figure 4-3. Change in the number of icing days.

Figure 4-4. Change in growing season length.

28

Figure 4-5. Change in cool nights based on the 10th percentile of control periods.

29

Figure 4-7. Change in the percentage warming nights based on 10th percentile of control periods.

Figure 4-6. Change in cool days.

30

Figure 4-8. Change in the percentage of warm days based on 90th percentile of control periods.

Figure 4-9. Change in the number of tropical nights.

31

Figure 4-10. Change in daily temperature range

Figure 4-11. Change in the number of old spell durations.

32

Figure 4-12. Change in warm spell duration.

Figure 4-13. Change in hottest days.

33

Figure 4-14. Change in coldest days.

Figure 4-15. Change in warmest nights.

34

Figure 4-16. Change in coldest night.

4.2 TRENDS IN ANNUAL PRECIPITATION INDICES

4.2.1 Precipitation in annual precipitation indices

Most of the precipitation indices show (partly significant) changes toward more

intense precipitation over Como region. For example, for the number of heavy

precipitation days (R10mm and R20mm) and the contribution form very wet days

(R95Ptot, See Figure 4-23). Globally averaged, to the indices display upward trends

during the 95 years. similar patterns of change are also found for the average intensity

of daily precipitation (See Figure 4-21) all precipitation based indices show larger

areas with significant trends toward wetter condition than area with drying trends.

The number of consecutive dry days (CDD, see Figure 4-19), a measure for

extremely dry conditions, also shows trend toward shorter duration of dry spells (i.e.,

fewer CDD) over Como lake region.

4.2.2 Trends in seasonal precipitation indices

Only two of the precipitation indices, RX1day and RX5day, have data available for

sub-annual timescales. We calculated the seasonal values of both indices as the

seasonal maxima of the monthly gridded fields. Seasonal trends are generally

comparable with annual trends. The annual maximum consecutive 5-day precipitation

35

amounts, for example, displays significant tendencies toward stronger extreme

precipitation over Como lake region (See in Figure 4-26). In this area, the increase in

extreme precipitation is visible across all seasons (See Figure 4-24), but mores

significant during winter and autumn.

Figure 4-18. Change in number of extreme precipitation days.

Figure 4-17. Change in the number of heavy precipitation days.

36

Figure 4-19. Change in consecutive dry days.

Figure 4-20. Change in consecutive wet days.

37

Figure 4-21. Change in annual total precipitation.

Figure 4-22. Change in total annual precipitation during wet days.

38

Figure 4-23. Change in fraction of annual total precipitation that exceeds 95th percentile based on control

period.

Figure 4-24. Change in the fraction of annual total precipitation that exceeds 99th percentile.

39

Figure 4-25. Change of maximum 1-day precipitation per each month.

Figure 4-26. Change of maximum consecutive 5-day precipitation per each month.

40

5.DISCUSSION

The individual indices provided insights into recent climate change in Lake Como

catchment and some clearly highlighted dramatic changes in the climate of the region

as a whole. For example, since 2030, showed strong, nearly linear increases in the

number of warm nights (90th percentile of minimum temperature).

Apparent increases in the incidence of worldwide annual warm extremes since 2015

mainly reflected the overall warming. However, there has been a larger decrease in

cold extremes, leading to a tendency to a decreasing total of all extremes worldwide.

This again shows a greater decrease in cold extremes than increase in warm

extremes. This may result from changes in regional atmospheric circulation. But in

high GHG emission scenario, the situation is more serious.

1. FD is assumed to decrease as a result of a general increase in local and

global mean temperature and Su is assumed to increase with the same

reason. FD and Su data effect on agriculture, gardening and recreation

especially in Como lake region.

2. ID is expected to decrease as a result of warming environment. GSL is

expected to increase as a direct result of increasing temperatures and

indirectly as a result of reductions in snow cover. GSL is important for

agriculture.

3. TN is a direct measure of the number of warm nights. The trend in Figure 4-5

and Figure 4-6 Show apparently increasing trend of warm nights, around 0.1

percentile more than nowadays until 2100. This indicator could reflect potential

harmful effects of the absence of nocturnal cooling, a main contributor to heat

related stress. Summer night-time warming is expected in a greenhouse gas

forced climate. This will partly come about as a clear sky radiative effect, partly

be a result of increased cloud cover from additional humidity being available

for nocturnal condensation. Main effect is expected in late summer, when

atmosphere holds maximum amount of moisture.

4. TR, the extraordinarily large number of TN days occurred in 2046, 2081 and

2096. The Figure 4-9 shows the large increase in tropical nights. It is important

for the human wellbeing that the body can cool down after a hot day. In

tropical nights, the temperature stays above 20 . During these night, it is

more difficult for human body to cool down, especially for elderly or sick

people. Therefore, an increase of tropical nights can lead to a rise of mortality.

The energy sector is affected by a higher electricity demand during summer

due to increased use of air conditioning.

41

5. DTR (See Figure 4-10) shows trend for Decrease in the DTR were identified in

the Como lake region, where large-area trends show that maximum

temperatures have remained constant or have increased only slightly,

whereas minimum temperatures have increased at a faster rate. “local” effects

such as urban growth, irrigation, desertification, and variations in local land

use can all affect the DTR.

6. TXn, TXn, TNx, TXx are respectively (a) hottest night (TXn) in , (b)coldest

day (TXn) in ，(c) warmest night (TNx) in and (d) hottest day (TXx) in .

Details of trend and time series calculations as described in Figure 4-8 -

Figure 4-11. Warming (but mostly weaker) trends are also found for

temperatures related to the warmest night (TNx) and the warmest day (TXx) in

Como lake region. On average globally, both Tnx and TXx have increased by

about 2 since 2015; over Como lake region, the increases in TNn are

stronger than increases in TXx. Consequently, the extreme temperature range

is reduced.

7. T90max and T10min are measure of intensity for extreme summer and winter

temperature conditions. Both cold days and warm days are in an increasing

trend. These are essential to describe timescale appropriate for variability

description and societally sensitive extremes (Jones et al. 1999). The change

in T90max temperature is higher in winter than summer whereas in the case of

T10max the change is higher in summer.

8. From CSDI changes and WSDI changes, the projected warm spell over Como

lake region seems to be increasing whereas the cold spells having decreasing

trend.

9. Rmm a direct measure of the number of very wet days. This indicator is highly

correlated with total annual and seasonal precipitation in most climates. Both

of the trends increase obviously in mid of this century, around 2045.

Greenhouse gas forcing Would lead to a perturbed climate with an enhanced

hydrological cycle. More water vapor available for condensation should give

rise to a clear increase in the number of days with heavy precipitation extreme

precipitation is found.

10. The number of consecutive dry days (CDD) and consecutive wet days (CWD),

a measure for extremely dry conditions and wet conditions, also show trends

toward shorter duration of dry spells and shorter duration of wet spells (i.e.,

fewer CDD and fewer CWD) over Como lake region. CDD, CWD and SDII

Effect on vegetation and ecosystems potential drought indicator. A decrease

trend in Figure 4-19 and Figure 4-20 reflect a wetter climate, due to more

frequent wet days. Under sustained greenhouse gas forcing, the interior of

continents may experience a general drying due to increased evaporation.

11. RX1day and RX5day are measure of short-term precipitation intensity

Potential flood indicator. The annual maximum consecutive 5day precipitation

42

amount, displays significant tendencies toward stronger extreme precipitation

in mid of this century with the value show in Figure 4-25 and Figure 4-26. In

this area, the increase in extreme precipitation is visible across all seasons,

but turn out to be more significant during spring and winter. We calculated the

seasonal values of both indices as the seasonal maxima of the monthly

gridded trends. Greenhouse gas forcing would lead to a perturbed climate with

an enhanced hydrological cycle. More water vapor available for condensation

should give rise to a clear increase in the total maximum amount of

precipitation for any given time period.

43

6.CONCLUSION

This study analyzed a number of state-of-the-art extreme climate indices using

CORDEX projection data under RCP 2.6 scenario. The analysis is done for Lake

Como catchment, which is observed to undergoing a transition possibly due to the

climate change impact. The results of indices can introduce the idea to stakeholders

that some growth patterns are more robust to climate change impacts than others.

These indices also brought the relationship between regional growth and water

availability to the forefront of discussions about managing the region’s future

development.

For decision makers, indices can be used to prepare for a range of future situation

and build flexibility into their decisions to best address uncertainties.

44

LIST OF FIGURES

Figure 1-1. Approaches to the development of global scenarios ................................................... 6

Figure 1-2. Population and GDP projections of the four scenarios underlying the RCPs........... 8

Figure 1-3. Development of primary energy consumption (direct equivalent) and oil

consumption for the different RCPs .................................................................................................... 9

Figure 1-4. The scenario matrix architecture ................................................................................... 10

Figure 1-5. Coupled Model Inter comparison Project Phase 5 (CMIP5) multi-model mean

projections ............................................................................................................................................. 11

Figure 2-1. Map of Lombardy region (Italy). The study area, i.e., Lake Como catchment, is

colored in violet located in northern part. ......................................................................................... 13

Figure 2-2. Hydrological features of Lake Como ............................................................................ 15

Figure 2-3. Trend analysis of the daily inflows over the time horizon 1946-2010 ...................... 16

Figure 3-1. Map of EOBS grid dataset for the study area ............................................................. 17

Figure 3-2. The map of study area within EURO-CORDEX grid domain. ................................... 19

Figure 4-1. Change in the number of frost days. ............................................................................ 26

Figure 4-2. Change in the number of summer days. ...................................................................... 26

Figure 4-3. Change in the number of icing days. ............................................................................ 27

Figure 4-4. Change in growing season length................................................................................. 27

Figure 4-5. Change in cool nights based on the 10th percentile of control periods. ................... 28

Figure 4-6. Change in cool days. ...................................................................................................... 29

Figure 4-7. Change in the percentage warming nights based on 10th percentile of control

periods. .................................................................................................................................................. 29

Figure 4-8. Change in the percentage of warm days based on 90th percentile of control periods.

................................................................................................................................................................ 30

Figure 4-9. Change in the number of tropical nights. ..................................................................... 30

Figure 4-10. Change in daily temperature range ............................................................................ 31

Figure 4-11. Change in the number of old spell durations. ........................................................... 31

Figure 4-12. Change in warm spell duration. ................................................................................... 32

Figure 4-13. Change in hottest days. ................................................................................................ 32

Figure 4-14. Change in coldest days. ............................................................................................... 33

Figure 4-15. Change in warmest nights. .......................................................................................... 33

Figure 4-16. Change in coldest night. ............................................................................................... 34

Figure 4-17. Change in the number of heavy precipitation days. ................................................. 35

Figure 4-18. Change in number of extreme precipitation days. .................................................... 35

Figure 4-19. Change in consecutive dry days. ................................................................................ 36

Figure 4-20. Change in consecutive wet days. ............................................................................... 36

Figure 4-21. Change in annual total precipitation. .......................................................................... 37

Figure 4-22. Change in total annual precipitation during wet days. ............................................. 37

45

Figure 4-23. Change in fraction of annual total precipitation that exceeds 95th percentile based

on control period. ................................................................................................................................. 38

Figure 4-24. Change in the fraction of annual total precipitation that exceeds 99th percentile.

................................................................................................................................................................ 38

Figure 4-25. Change of maximum 1-day precipitation per each month. ..................................... 39

Figure 4-26. Change of maximum consecutive 5-day precipitation per each month. ............... 39

LIST OF TABLES

Table 1-1. Overview of four RCPs ...................................................................................................... 7

Table 3-1. Feature of EOBS observatory dataset ........................................................................... 17

Table 3-2. Features of EURO-CORDEX dataset ............................................................................ 18

ACRONYMS

C

CCS Carbon Capture and Storage

CM Climate Modeling community

G

GHG Green House Gas

GCMs General Circulation Models

I

IAM Integrated Assessment Modeling community

IAV Impacts, Adaptation, and Vulnerability community

IPCC Intergovernmental Panel on Climate Change

R

RCP Representative Concentration Pathways

46

S

SRES Special Report on Emissions Scenarios

SPA Shared climate Policy Assumptions

SSP Shared Socioeconomic Reference Pathways

T

TPES Total Primary Energy Supply

U

UNEP United Nations environment program

UNFCCC United Nations Framework Convention on Climate Change

W

WMO World Meteorological Organization

47

BIBLIOGRAPHY

Giorgi F, Gutowski Jr W J. Regional dynamical downscaling and the CORDEX

initiative[J]. Annual Review of Environment and Resources, 2015, 40(1): 467.

Abeysirigunawardena DS, Walker IJ (2008) Sea level responses to climatic variability

and change in northern British Columbia. Atmosphere-Ocean 46:277Ð296

Abeysirigunawardena DS, Gilleland E, Bronaugh D, Wong W (2009) Extreme wind

regime responses to climate variability and change in the inner south coast of British

Columbia, Canada. Atmosphere-Ocean 47:41Ð62

Alexander L, Donat M (2011) The CLIMDEX project: creation of long-term global

gridded products for the analysis of temperature and precipitation extremes. In:

WCRP Open Science conference, Denver, Oct 2011

Alexander LV et al (2006) Global observed changes in daily climate extremes of

temperature and precipitation. J Geophys Res 111, D05109.

doi:10.1029/2005JD006290

Alexander LV, Uotila P, Nicholls N (2009) The inßuence of sea surface temperature

variability on global temperature and precipitation extremes. J Geophys Res-Atmos

114, D18116.

Alexander LV, Wang XL, Wan H, Trewin B (2011) SigniÞcant decline in storminess

over southeast Australia since the late 19th century. Aust Meteorol Oceanogr J

61:23Ð30

Alexandersson H, Schmith T, Iden K, Tuomenvirta H (1998) Long term variations of

the storm climate over NW Europe. Glob Atmos Ocean Syst 6:97Ð120

Allamano P, Claps P, Laio F (2009) Global warming increases ßood risk in

mountainous areas. Geophys Res Lett 36, L24404

Allan R, Tett S, Alexander LV (2009) Fluctuations of autumn-winter severe storms

over the British Isles: 1920 to present. Int J Climatol 29:357Ð371.

Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the

hydrologic cycle. Nature 419:224Ð232

Barnett TP et al (2008) Human-induced changes in the hydrology of the western

United States. Science 319:1080Ð1083

BŠrring L, Fortuniak K (2009) Multi-indices analysis of southern Scandinavian

storminess 1780Ð2005 and links to interdecadal variations in the NW Europe-North

Sea region. Int J Climatol 29:373Ð384 BŠrring L, von Storch H (2004) Scandinavian

storminess since about 1800. Geophys Res Lett

48

31:1790Ð1820

Barros V, Chamorro L, Coronel G, Baez J (2004) The major discharge events in the

Paraguay river: magnitudes, source regions, and climate forcings. J Hydrometeorol

5:1161Ð1170

Bates BC, Kundzewics ZW, Wu S, Palutikof JP (2008) Climate change and water.

Technical paper of the Intergovernmental Panel on Climate Change. IPCC Secretariat,

Geneva, 210 pp

Bengtsson L, Hodges KI, Roeckner E (2006) Storm tracks and climate change. J Clim

19:3518Ð3543 Bindoff NL et al (2007) Observations: oceanic climate change and sea

level. In: Solomon S et al (eds)

Blanchet J, Davison AC (2011) Spatial modeling of extreme snow depth. Ann Appl

Stat 5:1699Ð1725

Booth BBB, Dunstone NJ, Halloran PR, Andrews T, Bellouin N (2012) Aerosols

implicated as a prime driver of twentieth-century North Atlantic climate variability.

Nature 484:228Ð232 Brayshaw DB, Hoskins B, Blackburn M (2008) The storm-track

response to idealized SST pertur-

bations in an aquaplanet GCM. J Atmos Sci 65:2842Ð2860

Brooks HE (2004) On the relationship of tornado path length and width to intensity.

Weather Forecast 19:310Ð319

Burke EJ, Brown SJ (2008) Evaluating uncertainties in the projection of future drought.

J Hydrometeorol 9:292Ð299

Butler AH, Thompson DW, Heikes R (2010) The steady-state atmospheric circulation

response to climate change-like thermal forcings in a simple general circulation model.

J Clim 23:3474Ð3496.

Caesar J, Alexander L, Vose R (2006) Large-scale changes in observed daily

maximum and minimum temperatures: creation and analysis of a new gridded data

set. J Geophys Res 111, D05101.

Callaghan J, Power SB (2011) Variability and decline in the number of severe tropical

cyclones making land-fall over eastern Australia since the late nineteenth century.

Clim Dyn 37:647Ð662.

Camargo S, Ting M, Kushnir Y (2013) Inßuence of local and remote SST on North

Atlantic tropical cyclone potential intensity. Clim Dyn 40:1515Ð1529

Catto JL, Shaffrey LC, Hodges KJ (2010) Can climate models capture the structure of

extratropical cyclones? J Clim 23:1621Ð1635

49

Chen C-T, Knutson TR (2008) On the veriÞcation and comparison of extreme rainfall

indices from climate models. J Clim 21:1605Ð1621.

Chen J, Brissette FP, Leconte R (2011) Uncertainty of downscaling method in

quantifying the impact of climate change on hydrology. J Hydrol 401:190Ð202

Christensen JH et al (2007) Regional climate projections. In: Solomon S, Qin D,

Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change

2007: the physical science basis. Contribution of working group I to the fourth

assessment report of the Intergovernmental Panel on Climate Change. Cambridge

University Press, Cambridge, pp 847Ð940

Christidis N, Stott PA, Brown SJ, Hegerl GC, Caesar J (2005) Detection of changes in

temperature extremes during the second half of the 20th century. Geophys Res Lett

32, L20716

Christidis N, Stott PA, Brown SJ (2011) The role of human activity in the recent

warming of extremely warm daytime temperatures. J Clim 24:1922Ð1930

Church JA, Gregory JM, White NJ, Platten SM, Mitrovica XJ (2011) Understanding

and project-ing sea level change. Oceanography 24:130Ð143

Coles S (2001) An introduction to the statistical modeling of extreme values. Springer,

London, 208 pp. ISBN ISBN 1-85233-459-2

Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ et al (2011) The

twentieth century reanalysis project. Q J R Meteorol Soc 137:1Ð28.

Delgado JM, Apel H, Merz B (2009) Flood trends and variability in the Mekong river.

Hydrol Earth Syst Sci 6:6691Ð6719

Di Baldassarre G, Montanari A, Lins H, Koutsoyiannis D, Brandimarte L, Blšschl G

(2010) Flood fatalities in Africa: from diagnosis to mitigation. Geophys Res Lett 37,

L22402

Diffenbaugh NS, Trapp RJ, Brooks H (2008) Does global warming inßuence tornado

activity? Eos Trans Am Geophys Union 89:553

Donat M, Alexander L (2011) Uncertainties related to the production of gridded global

data sets of observed climate extreme indices. In: Abstract for WCRP Open Science

conference, Denver, Oct 2011

Elsner JB, Kossin JP, Jagger TH (2008) The increasing intensity of the strongest

tropical cyclones. Nature 455:92Ð95

Emanuel KA (2007) Environmental factors affecting tropical cyclone power dissipation.

J Clim 20:5497Ð5509

50

Emanuel K (2010) Tropical cyclone activity downscaled from NOAA-CIRES

reanalysis, 1908Ð 1958. J Adv Model Earth Syst 2:1.

Flannigan M, Logan K, Amiro B, Skinner W, Stocks B (2005) Future area burned in

Canada. Clim Chang 72:1Ð16

Fleig AK, Tallasken LM, Hisdal H, Demuth S (2006) A global evaluation of streamßow

drought characteristics. Hydrol Earth Syst Sci 10:535Ð552

Fogt RL, Perlwitz J, Monaghan AJ, Bromwich DH, Jones JM, Marshall GJ (2009)

Historical SAM variability. Part II: twentieth-century variability and trends from

reconstructions, observations, and the IPCC AR4 models. J Clim 22:5346Ð5365

Frei C, SchŠr C (2001) Detection probability of trends in rare events: theory and

application to heavy precipitation in the Alpine region. J Clim 14:1568Ð1584

Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein Tank AMG,

Peterson T (2002) Observed coherent changes in climatic extremes during the

second half of the twentieth cen-tury. Clim Res 19:193Ð212

Giannini A, Biasutti M, Verstraete MM (2008) A climate model-based review of

drought in the Sahel: desertiÞcation, the re-greening and climate change. Glob Planet

Chang 64:119Ð128 Gillett NP, Stott PA (2009) Attribution of anthropogenic inßuence

on seasonal sea level pressure.

Gillett NP, Weaver AJ, Zwiers FW, Wehner MF (2004) Detection of volcanic inßuence

on global precipitation. Geophys Res Lett 31, L12217.

Greeves CZ, Pope VD, Stratton RA, Martin GM (2007) Representation of Northern

Hemisphere winter storm tracks in climate models. Clim Dyn 28:683Ð702

Hanel M, Buishand TA, Ferro CAT (2009) A nonstationary index ßood model for

precipitation extremes in transient regional climate model simulations. J Geophys Res

114, D15107.

Hanesiak JM et al (2011) Characterization and summary of the 1999Ð2005 Canadian

Prairie drought. Atmosphere-Ocean 49:421Ð452.

Hanlon H, Hegerl GC, Tett SFB, Smith DM (2012a) Can a decadal forecasting system

predict temperature extreme indices? J Clim, in press.

Hanlon H, Morak S, Hegerl GC (2012b) Detection and prediction of mean and

extreme European summer temperatures with a CMIP5 multi-model ensemble. J

Geophys Res, submitted

Hanna E, Cappelen J, Allan R, Jonsson T, Le Blancq F, Lillington T, Hickey K (2008)

New insights into North European and North Atlantic surface pressure variability,

storminess, and related climatic change since 1830. J Clim 21:6739Ð6766

51

Hannaford J, Marsh T (2006) An assessment of trends in UK runoff and low ßows

using a network of undisturbed catchments. Int J Climatol 26:1237Ð1253

Hannaford J, Marsh TJ (2008) High-ßow and ßood trends in a network of undisturbed

catchments in the UK. Int J Climatol 28:1325Ð1338

Hanson CE, Palutikof JP, Davies TD (2004) Objective cyclone climatologies of the

North Atlantic Ð a comparison between the ECMWF and NCEP reanalyses. Clim Dyn

22:757Ð769 Harper B, Hardy T, Mason L, Fryar R (2009) Developments in storm tide

modelling and risk assessment in the Australian region. Nat Hazard 51:225Ð238

Heaton MJ, Katzfuss M, Ramachandar S, Pedings K, Gilleland E,

Mannshardt-Shamseldin E, Smith RL (2010) Spatio-temporal models for large-scale

indicators of extreme weather. Environmetrics 22:294Ð303

Hegerl GC, Zwiers FW, Stott PA, Kharin VV (2004) Detectability of anthropogenic

changes in annual temperature and precipitation extremes. J Clim 17:3683Ð3700

Hegerl GC, Zwiers FW, Braconnot P, Gillett NP, Luo Y, Marengo Orsini JA, Nicholls N,

Penner JE, Stott PA (2007) Understanding and attributing climate change. In:

Solomon S et al (eds) The physical science basis. Contribution of working group I to

the fourth assessment report of the Intergovernmental Panel on Climate Change.

Cambridge University Press, Cambridge/New York

Hegerl GC, Hanlon H, Beierkuhnlein C (2011) Elusive extremes. Nat Geosci

4:142Ð143

Hegerl GC, Hoegh-Guldberg O, Casassa G, Hoerling MP, Kovats RS, Parmesan C,

Pierce DW, Stott PA (2010) Good practice guidance paper on detection and

attribution related to anthropo-genic climate change. In: Stocker TF, Field CB, Qin D,

Barros V, Plattner G-K, Tignor M, Midgley PM, Ebi KL (eds) Meeting report of the

Intergovernmental Panel on Climate Change expert meeting on detection and

attribution of anthropogenic climate change. IPCC working group I technical support

unit, University of Bern, Bern

Heim RR (2002) A review of twentieth-century drought indices used in the United

States. Bull Am Meteorol Soc 83:1149Ð1165

Held IM, Delworth TL, Lu J, Findell KL, Knutson TR (2005) Simulation of Sahel

drought in the 20th and 21st centuries. Proc Natl Acad Sci USA 102:17891Ð17896.

doi:10.1073_pnas.0509057102 Hidalgo HG et al (2009) Detection and attribution of

streamßow timing changes to climate change in the Western United States. J Clim

22:3838Ð3855

52

Hoerling MP, Hurrell J, Eischeid J, Phillips A (2006) Detection and attribution of

twentieth-century Northern and Southern African rainfall change. J Clim

19:3989Ð4008

Hoerling M, Quan X, Eischeid J (2009) Distinct causes for two principal US droughts

of the 20th century. Geophys Res Lett 36, L19708

Hohenegger C, Brockhaus P, Bretherton CS, SchŠr C (2009) The soil

moisture-precipitation feedback in simulations with explicit and parameterized

convection. J Clim 22:5003Ð5020 Holland GJ, Webster PJ (2007) Heightened tropical

cyclone activity in the North Atlantic: natural variability or climate trend? Philos Trans

R Soc A 365:2695Ð2716

Hossain F, Jeyachandran I, Pielke R Jr (2009) Have large dams altered extreme

precipitation patterns. Eos Trans Am Geophys Union 90:453

IPCC (2007a) Climate change 2007: the physical science basis. In: Solomon S et al

(eds) Contribution of working group I to the fourth assessment report of the

Intergovernmental Panel on Climate Change. Cambridge University Press,

Cambridge/New York, 996 pp

IPCC (2007b) Climate change 2007: impacts, adaptation and vulnerability. In: Parry

ML et al (eds) Contribution of working group II to the fourth assessment report of the

Intergovernmental Panel on Climate Change. Cambridge University Press,

Cambridge, 976 pp

IPCC (2012) Summary for policymakers. In: Field CB, Barros V, Stocker TF, Qin D,

Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M,

Midgley PM (eds) Managing the risks of extreme events and disasters to advance

climate change adaptation. A special report of working groups I and II of the

Intergovernmental Panel on Climate Change. Cambridge University Press,

Cambridge/New York, pp 1Ð19

Jiang T, Kundzewicz ZW, Su B (2008) Changes in monthly precipitation and ßood

hazard in the Yangtze River Basin, China. Int J Climatol 28:1471Ð1481

Johnson NC, Xie S-P (2010) Changes in the sea surface temperature threshold for

tropical convection. Nat Geosci 3:842Ð845

Karl TR (1983) Some spatial characteristics of drought duration in the United States. J

Clim Appl Meteorol 22:1356Ð1366

Karl TR, Knight RW (1997) The 1995 Chicago heat wave: how likely is a recurrence?

Bull Am Meteorol Soc 78:1107Ð1119

Karl TR, Knight RW, Easterling DR, Quayle RG (1996) Indices of climate change for

the United States. Bull Am Meteorol Soc 77:279Ð292

53

Katz R, Parlange M, Naveau P (2002) Extremes in hydrology. Adv Water Resour

25:1287Ð1304 Kay AL, Davies HN, Bell VA, Jones RG (2009) Comparison of

uncertainty sources for climate

change impacts: ßood frequency in England. Clim Chang 92:41Ð63

Kenyon J, Hegerl GC (2008) Inßuence of modes of climate variability on global

temperature extremes. J Clim 21:3872Ð3889

Kenyon J, Hegerl GC (2010) Inßuence of modes of climate variability on global

precipitation extremes. J Clim 23:6248Ð6262

Kharin VV, Zwiers FW (2000) Changes in the extremes in an ensemble of transient

climate simulation with a coupled atmosphereÐocean GCM. J Clim 13:3760Ð378

Kharin VV, Zwiers FW (2005) Estimating extremes in transient climate change

simulations. J Clim 18:1156Ð1173

Kharin VV, Zwiers FW, Zhang X, Hegerl GC (2007) Changes in temperature and

precipitation extremes in the IPCC ensemble of global coupled model simulations. J

Clim 20:1419Ð1444 Kistler R et al (2001) The NCEP-NCAR 50-year reanalysis:

monthly means CD-ROM and documentation. Bull Am Meteorol Soc 82:247Ð267

Klawa M, Ulbrich U (2003) A model for the estimation of storm losses and the

identiÞcation of severe winter storms in Germany. Nat Hazard Earth Syst Sci

3:725Ð732

Knutson TR, Tuleya RE (2004) Impact of CO2-induced warming on simulated

hurricane intensity and precipitation: sensitivity to the choice of climate model and

convective parameterization. J Clim 17:3477Ð3495

Knutson TR, Sirutis JJ, Garner ST, Vecchi GA, Held IM (2008) Simulated reduction in

Atlantic hurricane frequency under twenty-Þrst- century warming conditions. Nat

Geosci 1:359Ð364

Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin

JP, Srivastava AK, Sugi M (2010) Tropical cyclones and climate change. Nat Geosci

3:157Ð163

Kossin JP, Knapp KR, Vimont DJ, Murnane RJ, Harper BA (2007) A globally

consistent reanalysis of hurricane variability and trends. Geophys Res Lett 34,

L04815. doi:10.1029/2006GL028836 Kossin JP, Camargo SJ, Sitkowski M (2010)

Climate modulation of North Atlantic hurricane tracks. J Clim 23:3057Ð3076

Koster RD et al (2004) Regions of strong coupling between soil moisture and

precipitation. Science 305:1138Ð1140

54

Koster RD, Guo ZC, Yang RQ, Dirmeyer PA, Mitchell K, Puma MJ (2009) On the

nature of soil moisture in land surface models. J Clim 22:4322Ð4335

Lambert SJ, Fyfe JC (2006) Changes in winter cyclone frequencies and strengths

simulated in enhanced greenhouse warming experiments: results from the models

participating in the IPCC diagnostic exercise. Clim Dyn 26:713Ð728

Lambert FH, Gillett NP, Stone DA, Huntingford C (2005) Attribution studies of

observed land precipitation changes with nine coupled models. Geophys Res Lett 32,

L18704. doi:10.1029/2 005GL023654

Landsea CW (2007) Counting Atlantic tropical cyclones back to 1900. Eos Trans Am

Geophys Union 88:197Ð202

Landsea CW, Anderson C, Charles N, Clark G, Dunion J, Fernandez-Partagas J,

Hungerford P, Neumann C, Zimmer M (2004) The Atlantic hurricane database

re-analysis project: documen-tation for the 1851Ð1910 alterations and additions to the

HURDAT database. In: Murnane RJ, Liu KB (eds) Hurricanes and Typhoons: past,

present and future. Columbia University Press, New York, pp 177Ð221

Landsea CW, Harper BA, Hoarau K, Knaff JA (2006) Can we detect trends in extreme

tropical cyclones? Science 313:452Ð454

Lin N, Emanuel K, Oppenheimer M, Vannarcke E (2012) Physically based

assessment of hurricane surge threat under climate change. Nat Clim Change

2:462Ð467. doi: 10.1038/NCLIMATE1389 Lorenz C, Kunstmann H (2012) The

hydrological cycle in three state-of-the-art reanalyses: intercomparison and

performance analysis. J Hydrometeorol 13(5):1397Ð1420

Mann ME, Emanuel KA (2006) Atlantic hurricane trends linked to climate change. Eos

Trans Am Geophys Union 87:233Ð241384 F.W. Zwiers et al.

Mann ME, Emanual KA, Holland GJ, Webster PJ (2007) Atlantic tropical cyclones

revisited. Eos Trans Am Geophys Union 88:349Ð350

Mannshardt-Shamseldin EC, Smith RL, Sain SR, Mearns LD, Cooley D (2010)

Downscaling extremes: a comparison of extreme value distributions in point-source

and gridded precipita-tion data. Ann Appl Stat 4:484Ð502

Manton MJ et al (2001) Trends in extreme daily rainfall and temperature in southeast

Asia and the South PaciÞc: 1916Ð1998. Int J Climatol 21:269Ð284

McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and

duration to time scales. Preprints, Eighth conference on applied climatology,

American Meteorological Society, Anaheim, pp 179Ð184

55

Meehl GA et al (2007a) Global climate projections. In: Solomon S et al (eds) The

physical science basis. Contribution of working group I to the fourth assessment

report of the Intergovernmental Panel on Climate Change. Cambridge University

Press, Cambridge/New York

Meehl GA et al (2007b) The WCRP CMIP3 multi-model dataset: a new era in climate

change research. Bull Am Meteorol Soc 88:1383Ð1394

Mendelsohn R, Emanuel K, Chonabayashi S, Bakkensen L (2012) The impact of

climate change on global tropical cyclone damage. Nat Clim Change 2:205Ð209.

doi:10.1038/nclimate1357 MenŽndez M, Woodworth PL (2010) Changes in extreme

high water levels based on a quasi-global

tide-gauge dataset. J Geophys Res 115, C10011

Mesinger F et al (2006) North American regional reanalysis. Bull Am Meteorol Soc

87:343Ð360

Mitrovica JX, Tamisiea ME, Ivins ER, Vermeersen LLA, Milne GA, Lambeck K (2010)

Surface mass loading on a dynamic earth: complexity and contamination in the

geodetic analysis of global sea-level trends. In: Church JA et al (eds) Understanding

sea-level rise and variability. Wiley-Blackwell, Chichester, pp 285Ð325

Morak S, Hegerl GC, Kenyon J (2011) Detectable regional changes in the number of

warm nights. Geophys Res Lett 38, L17703

Morak S, Hegerl GC, Christidis N (2013) Detectable changes in temperature

extremes. J Clim 26:1561Ð1574

Mousavi ME, Irish JL, Frey AE, Olivera F, Edge BL (2011) Global warming and

hurricanes: the potential impact of hurricane intensiÞcation and sea level rise on

coastal ßooding. Clim Chang 104:575Ð597

Mudelsee M, Borngen M, Tetzlaff G, Grunewald U (2003) No upward trends in the

occurrence of extreme ßoods in central Europe. Nature 425:166Ð169

Mueller B, Seneviratne SI (2012) Hot days induced by precipitation deÞcits at the

global scale. Proc Natl Acad Sci USA 109:12398Ð12403.

doi:10.1073/pnas.1204330109

Nicholls N, Alexander L (2007) Has the climate become more variable or extreme?

Progress 1992Ð2006. Prog Phys Geogr 31:1Ð11Extremes 385

Nicholls N, Larsen S (2011) Impact of drought on temperature extremes in Melbourne,

Australia. Aust Meteorol Oceanogr J 61:113Ð116

Noake K, Polson D, Hegerl GC, Zhang X (2011) Changes in seasonal land

precipitation during the latter twentieth century. Geophys Res Lett 39, L03706

56

Orlowsky B, Seneviratne SI (2012) Global changes in extremes events: regional and

seasonal dimension. Clim Chang 110:669Ð696.

Otto FE, Massey N, van Oldenburg GJ, Jones RG, Allen MR (2012) Reconciling two

approaches to attribution of the 2010 Russian heat wave. Geophys Res Lett 39,

L04702. doi: 10.1029/201 1GL050422

Page CM, Nicholls N, Plummer N, Trewin BC, Manton MJ, Alexander L, Chambers LE,

Choi Y, Collins DA, Gosai A, Della-Marta P, Haylock MR, Inape K, Laurent V,

Maitrepierre L, Makmur EEP, Nakamigawa H, Ouprasitwong N, McGree S, Pahalad J,

Salinger MJ, Tibig L, Tran TD, Vediapan K, Zhai P (2004) Data rescue in the

South-east Asia and South PaciÞc region: challenges and opportunities. Bull Amer

Meteorol Soc 85:1483Ð1489

Pall P, Aina T, Stone DA, Stott PA, Nozawa T, Hilbberts AGJ, Lohmann D, Allen MR

(2011) Anthropogenic greenhouse gas contribution to ßood risk in England and Wales

in autumn 2000. Nature 470:382Ð385

Palmer WC (1965) Meteorological drought. Research paper 45, US Department of

Commerce, Weather Bureau, Washington, DC, 58 pp. Available from NOAA Library

and Information Services Division, Washington, DC, 20852

Polson D, Hegerl GC, Zhang X, Osborn T (2013) Causes of robust seasonal land

precipitation changes. J Clim, in press

Portmann RW, Solomon S, Hegerl GC (2009) Spatial and seasonal patterns in

climate change, temperatures, and precipitation across the United States. Proc Natl

Acad Sci USA 106:7324Ð7329.

Prudhomme C, Davies H (2009) Assessing uncertainties in climate change impact

analyses on the river ßow regimes in the UK. Part 2: future climate. Clim Chang

93:197Ð222

Ramsay HA, Sobel AH (2011) The effects of relative and absolute sea surface

temperature on tropical cyclone potential intensity using a single column model. J

Clim 24:183Ð193

Randall DA et al (2007) Climate models and their evaluation. In: Solomon S et al (eds)

The physical science basis. Contribution of working group I to the fourth assessment

report of the Intergovernmental Panel on Climate Change. Cambridge University

Press, Cambridge/ New York

Roderick ML, Rotstayn LD, Farquhar GD, Hobbins MT (2007) On the attribution of

changing pan evaporation. Geophys Res Lett 34, L17403.

Ropelewski CF, Halpert MS (1987) Global and regional scale precipitation patterns

associated with the El Ni–o/Southern Oscillation. Mon Weather Rev 115:1606Ð1626

57

Rosenzweig C et al (2007) Assessment of observed changes and responses in

natural and managed systems. In: Parry ML et al (eds) Impacts, adaptation and

vulnerability. Contribution of work-ing group II to the fourth assessment report of the

Intergovernmental Panel on Climate Change. Cambridge University Press,

Cambridge/New York

Sang H, Gelfand AE (2010) Continuous spatial process models for spatial extreme

values. J Agric Biol Environ Stat 15:49Ð65

Santer BD et al (2006) Forced and unforced ocean temperature changes in Atlantic

and PaciÞc tropical cyclogenesis regions. Proc Natl Acad Sci USA 103:13905Ð13910

Schlather M (2002) Models for stationary max-stable random Þelds. Extremes

5:33Ð44 Schmidt H, von Storch H (1993) German Bight storms analysed. Nature

365:791

Schmith T, Kaas E, Li T-S (1998) Northeast Atlantic winter storminess 1875Ð1995

re-analysed. Clim Dyn 14:529Ð536

Schubert S et al (2009) A US CLIVAR project to assess and compare the responses

of global climate models to drought-related SST forcing patterns: overview and results.

J Clim 22:5251Ð5272 Seneviratne SI, LŸthi D, Litschi M, SchŠr C (2006)

Land-atmosphere coupling and climate changein Europe. Nature 443:205Ð209

ShefÞeld J, Wood EF (2008) Global trends and variability in soil moisture and drought

charac-teristics, 1950Ð2000, from observation-driven simulations of the terrestrial

hydrologic cycle. J Clim 21:432Ð458

Sherwood SC, Huber M (2010) An adaptability limit to climate change due to heat

stress. Proc Natl Acad Sci USA 107(21):9552Ð9555

Shiklomanov AI, Lammers RB, Rawlins MA, Smith LC, Pavelsky TM (2007) Temporal

and spatial variations in maximum river discharge from a new Russian data set. J

Geophys Res 112:G04S53

Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013a) Climate extreme

indices in the CMIP5 multi-model ensemble. Part 1: model evaluation in the present

climate. J Geophys Res 118

Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013b) Climate extreme

indices in the CMIP5 multi-model ensemble. Part 2: future climate projections. J

Geophys Res 118

Smith RL (1986) Extreme value theory based on the r-largest annual events. J Hydrol

86:27Ð43 Smith RL (1990) Max-stable processes and spatial extremes. Unpublished

manuscript. Available at http://www.stat.unc.edu/postscript/rs/spatex.pdf

58

Smits A, Klein Tank AMG, Kšnnen GP (2005) Trends in storminess over the

Netherlands, 1962Ð 2002. Int J Climatol 25:1331Ð1344

Solomon S et al (2007) Technical summary. In: Solomon S et al (eds) Climate change

2007: the physical science basis. Contribution of working group I to the fourth

assessment report of the Intergovernmental Panel on Climate Change. Cambridge

University Press, Cambridge/ New York Extremes 387

Stahl K, Hisdal H, Hannaford J, Tallaksen LM, van Lanen HAJ, Sauquet E, Demuth S,

Fendekova M, Jodar J (2010) Streamßow trends in Europe: evidence from dataset of

near-natural catch-ments. Hydrol Earth Syst Sci 14:2367Ð2382

Steadman RG (1979) The assessment of sultriness. Part I: a temperature-humidity

index based on human physiology and clothing science. J Appl Meteorol 18:861Ð873

Stott PA, Stone DA, Allen MR (2004) Human contribution to the European heatwave

of 2003. Nature 432:610Ð614

Stott PA et al (2012) Attribution of weather and climate-related extreme events. In:

WCRP Open Science conference: climate research in service to society, World

Climate Research Programme, Denver (this volume)

Sugi M, Murakami H, Yoshimura J (2009) A reduction in global tropical cyclone

frequency due to global warming. SOLA 5:164Ð167

Ting M, Kushnir Y, Seager R, Li C (2009) Forced and internal 20th century SST

trends in the North Atlantic. J Clim 22:1469Ð1481

Trapp RJ et al (2005) Tornadoes from squall lines and bow echoes. Pt I:

climatological distribution. Weather Forecast 20:23Ð34

Trenberth KE et al (2007) Observations: surface and atmospheric climate change. In:

Solomon S et al (eds) The Physical science basis. Contribution of working group I to

the fourth assessment report of the Intergovernmental Panel on Climate Change.

Cambridge University Press, Cambridge/New York

Ulbrich U, Pinto JG, Kupfer H, Leckebusch GC, Spangehl T, Reyers M (2008)

Changing northern hemisphere storm tracks in an ensemble of IPCC climate change

simulations. J Clim 21:1669Ð1679

Uppala SM et al (2005) The ERA ‐ 40 re ‐ analysis. Q J R Meteorol Soc

131:2961Ð3012.

van Pelt SC, Kabat P, ter Maat TW, van den Hurk BJJM, Weerts AH (2009) Discharge

simulations performed with a hydrological model using bias corrected regional climate

model input. Hydrol Earth Syst Sci 13:2387Ð2397

59

Van Wagner C (1987) Development and structure of the Canadian forest Þre weather

index system. Technical report #35, Canadian Forest Service

Vannitsem S, Naveau P (2007) Spatial dependences among precipitation maxima

over Belgium. Nonlinear Process Geophys 14:621Ð630

Vautard R, Cattiaux J, Yiou P, Thepaut J-N, Ciais P (2010) Northern Hemisphere

atmospheric still-ing partly attributed to an increase in surface roughness. Nat Geogr

3:756Ð761. doi:10.1038/ NGEO979

Villarini G, Vecchi GA (2013) Projected increases in North Atlantic tropical cyclone

intensity from CMIP5 models. J Clim 26:3231Ð3240

Villarini G, Serinaldi F, Smith JA, Krajewski WF (2009) On the stationarity of annual

ßood peaks in the continental United States during the 20th century. Water Resour

Res 45, W08417

Wang XL, Feng Y, Compo GP, Swail VR, Zwiers FW, Allan RJ, Sardeshmukh PD

(2012) Trends and low frequency variability of extra-tropical cyclone activity in the

ensemble of twentieth century reanalysis. Clim Dyn

Webster PJ, Holland GJ, Curry JA, Chang HR (2005) Changes in tropical cyclone

number, dura-tion, and intensity in a warming environment. Science 309:1844Ð1846

Wehner MF (2013) Very extreme seasonal precipitation in the NARCCAP ensemble:

model performance and projections. Clim Dyn 40(1Ð2):59Ð80.

doi:10.1007/s00382-012-1393-1 Wehner MF, Smith RL, Bala G, Duffy P (2010) The

effect of horizontal resolution on simulation of very extreme precipitation events in a

global atmospheric model. Clim Dyn 34:241Ð247

Wells N, Goddard S, Hayes MH (2004) A self-calibrating Palmer drought severity

index. J Clim 17:2335Ð2351

Zhang R, Delworth T (2009) A new method for attributing climate variations over the

Atlantic Hurricane BasinÕs main development region. Geophys Res Lett 36, L06701.

doi: 10.1029/200 9GL037260

Zhang X, Zwiers FZ (2013) Statistical indices for diagnosing and detecting changes in

extremes. In: Agha Kouchak et al (eds) Extremes in a changing climate. Water

Science and Technology Library 65. Springer, Dordrecht.

Zhang X, Zwiers FW, Li G (2004) Monte Carlo experiments on the detection of trends

in extreme values. J Clim 17:1945Ð1952

Zhang X, Hegerl GC, Zwiers FW (2005) Avoiding inhomogeneity in percentile-based

indices of temperature extremes. J Clim 18:1641Ð1651

60

Zhang X, Alexander LV, Hegerl GC, Klein Tank A, Peterson TC, Trewin B, Zwiers FW

(2011) Indices for monitoring changes in extremes based on daily temperature and

precipitation data. Wiley Interdiscip Rev Clim Change 2:851Ð870.