Download 2. Observed climatology and sensitivity of runoff

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

German Climate Action Plan 2050 wikipedia , lookup

2009 United Nations Climate Change Conference wikipedia , lookup

Heaven and Earth (book) wikipedia , lookup

Soon and Baliunas controversy wikipedia , lookup

Michael E. Mann wikipedia , lookup

Climatic Research Unit email controversy wikipedia , lookup

Global warming hiatus wikipedia , lookup

Atmospheric model wikipedia , lookup

Global warming controversy wikipedia , lookup

ExxonMobil climate change controversy wikipedia , lookup

Climate resilience wikipedia , lookup

Fred Singer wikipedia , lookup

Climate change denial wikipedia , lookup

Climate engineering wikipedia , lookup

Global warming wikipedia , lookup

Climatic Research Unit documents wikipedia , lookup

Climate change adaptation wikipedia , lookup

Politics of global warming wikipedia , lookup

Economics of global warming wikipedia , lookup

Effects of global warming on human health wikipedia , lookup

Instrumental temperature record wikipedia , lookup

Climate sensitivity wikipedia , lookup

Citizens' Climate Lobby wikipedia , lookup

Climate change feedback wikipedia , lookup

Climate governance wikipedia , lookup

Climate change in Saskatchewan wikipedia , lookup

Climate change in Tuvalu wikipedia , lookup

Carbon Pollution Reduction Scheme wikipedia , lookup

Solar radiation management wikipedia , lookup

Climate change and agriculture wikipedia , lookup

Media coverage of global warming wikipedia , lookup

Effects of global warming wikipedia , lookup

Climate change in the United States wikipedia , lookup

Scientific opinion on climate change wikipedia , lookup

Attribution of recent climate change wikipedia , lookup

Global Energy and Water Cycle Experiment wikipedia , lookup

Public opinion on global warming wikipedia , lookup

Climate change and poverty wikipedia , lookup

Effects of global warming on humans wikipedia , lookup

Surveys of scientists' views on climate change wikipedia , lookup

Climate change, industry and society wikipedia , lookup

General circulation model wikipedia , lookup

IPCC Fourth Assessment Report wikipedia , lookup

Transcript
Supplementary Material for
Snowpack and Runoff Response to Climate
Change in Owens Valley and Mono Lake
Watersheds
Mariza Costa-Cabral, Sujoy B. Roy, Edwin P. Maurer, William B. Mills, and Limin Chen
Table of Contents
0. Regional map and topographic map of the Owens Lake and Mono Lake
watersheds ............................................................................................................... 2
1. Brief review of literature: Observed hydrologic and ecologic changes associated
with climate change in the Western U.S. .................................................................. 4
2. Observed climatology and sensitivity of runoff to climate in the main runoffproducing areas of the watersheds of Owens Lake and Mono Lake in the
historical period 1950-1999 ...................................................................................... 6
3. The 16 global climate models studied ....................................................................... 10
4. Statistical downscaling of GCM projections of temperature and precipitation ........... 11
5. VIC Model calibration and verification ....................................................................... 15
6. Relationship between mean annual runoff and mean annual precipitation for each
individual GCM ....................................................................................................... 23
Literature References .................................................................................................... 30
1
0. REGIONAL MAP AND TOPOGRAPHIC MAP
OF THE OWENS LAKE AND MONO LAKE
WATERSHEDS
Figure 0-1: The watersheds of Owens Lake and Mono Lake in the Eastern Sierra Nevada, and the Los Angeles
Aqueducts.
2
Figure 0-2: Topography of the Owens Lake and Mono Lake watersheds. Map produced by Tetra Tech, Inc.
The runoff-producing areas on the eastern slopes were modeled and are depicted in Figure 5-1.
3
1. BRIEF REVIEW OF LITERATURE:
OBSERVED HYDROLOGIC AND ECOLOGIC
CHANGES ASSOCIATED WITH CLIMATE
CHANGE IN THE
WESTERN U.S.
Climate change impact studies on snow-melt dominated watersheds show some consistency in the types of
projected impacts (Adam el at., 2009). Past studies in the Western US have identified some of the types of
changes that are expected to occur, although these may not occur consistently across all regions. Typical
changes include earlier snowmelt, lower snowpack accumulation, changes in the quantity and timing of winter
precipitation, and greater extremes of precipitation events and droughts. These studies have been based on the
instrumental record from the latter part of the 20th Century (e.g., Cayan, 1996; Groisman et al., 2004; Hamlet
et al., 2005; Knowles et al., 2006; Mote et al., 2005) and on global climate model (GCM) projections for the
21st Century (e.g., Cayan et al., 2008; Barnett et al., 2005, 2008; Maurer, 2007b,c).
Research on recent hydrologic and ecological changes associated with climate in the North American West
and Sierra Nevada has documented the following observations:

A shift in snowmelt timing toward earlier dates (by 5 to 30 days) (Gleick et al, 1987; Lettenmaier and Gan,
1990; Roos, 1987, 1991; Cayan et al., 2001; Wahl, 1992; Aguado et al., 1992; Pupacko, 1993; Dettinger
and Cayan, 1995; Johnson et al., 1999; Brown, 2000; Dettinger et al., 2004b; Stewart et al., 2005; Mote et
al., 2005; Regonda et al., 2005; Knowles et al., 2006; Maurer et al., 2007c; Coats, 2010);

A shift from snowfall to rainfall (ACPI, 2004; Hamlet et al., 2005; Regonda et al., 2005; Knowles et al.,
2006);
4

A decline in snow water content in many high-elevation locations including the northern Sierra Nevada,
but a rising trend at the highest elevations in the southern Sierra Nevada (Johnson et al., 1999; Mote, 2003;
Groisman et al., 2004; Mote et al., 2005; Regonda et al., 2005; Knowles et al., 2006);

The attribution of the observed decline in western U.S. winter snowpack to climate change forced by
anthropogenic greenhouse gases and aerosols (Pierce et al., 2008; see also Moser et al., 2009).

Earlier onset of spring (Cayan et al., 2001; Wolkovich et al., 2012);

The impacts of the above changes on water supply in California and throughout the West (Hamlet et al.,
2005; Mote et al., 2005; Barnett et al., 2008);

Increasing risk of wildfire (Westerling et al., 2006; Running, 2006; Brown et al., 2004). In 2007, the
catastrophic Angora Fire in the Tahoe basin provided a stunning illustration of what can occur when
increased fuel availability, associated with legacy overstocked stands, combines with dry terrestrial
moisture conditions to increase fire hazard.
Parallel observations have been made in other high-elevation environments in the U.S. (e.g. in New England,
in studies by Hodgkins et al., 2003 and Hodgkins and Dudley, 2006; and in the American Cordillera, as
reported in Pederson et al., 2011) and elsewhere (e.g. in the Pyrenees, in the study by López-Moreno and
García-Ruiz, 2004).
5
2. OBSERVED CLIMATOLOGY AND
SENSITIVITY OF RUNOFF TO CLIMATE IN
THE MAIN RUNOFF-PRODUCING AREAS
OF THE WATERSHEDS OF
OWENS LAKE
MONO LAKE IN THE HISTORICAL
PERIOD 1950-1999
AND
To represent observed climatology we use the observation-based 1950-2005 distributed time series of
temperature and precipitation developed by Maurer et al. (2002). This observational dataset, which has
1/8˚ (approx. 12 km) spatial resolution and daily temporal resolution, was used as the basis for statistical
downscaling of GCM projections (1950-1999), and also for the calibration and verification of the VIC
hydrologic model (1950-2005).
Figure 2-1 displays the mean annual temperature and precipitation values observed in water years
1951-1999, averaged over the Owens-Mono watersheds’ runoff producing area, i.e., the eastern Sierra
slopes (depicted in the section on model calibration). For example, the year labeled as 1951 represents
the period from October 1, 1950, through September 30, 1951. Total annual precipitation in these 49
years varied from 302 mm (1977) to 1064 mm (1969), i.e., by a factor of 3.5. The range in mean annual
temperature was 0.29°C to 3.0°C. There is a weak negative correlation (R2=0.12) between the two
variables, warmer years appearing less likely to be wet.
6
The total annual runoff observed in water years 1951-2005 is displayed in Figure 2-2. Annual runoff had
a mean value of 318 mm (0.475 MAF) and covered a wide range, from 155 mm (in 1977, the lowest
precipitation year) to 628 mm (in 1969, the highest precipitation year), corresponding to a factor of 4.
Figure 2-1: Mean annual temperature and precipitation values observed in 1951-1999 (i.e., from water
year 1950-51 through 1998-99), averaged over the eastern Sierra slopes of the Owens-Mono
watersheds. These are gridded data (1/8° resolution for the entire watershed area based on
observations at point locations. Data source: Maurer et al. (2002).
7
Figure 2-2: Mean annual unimpaired runoff observed in water years 1951-2005, averaged over the
eastern Sierra slopes of the Owens-Mono watersheds. The 55-year average is 318 mm (0.475 MAF) per
year. Data source: Los Angeles Department of Water and Power.
Figure 2-3: Observed water-year runoff and precipitation in the eastern Sierra slopes of the Owens-Mono
watersheds, in 1951-1999. Each of the 49 points corresponds to a water year. The best-fit straight line is
shown, with a slope of 0.574 and an intercept of -13 mm. The linear correlation is high (R2=0.887).
8
Figure 2-4: Observed annual runoff in the plotted against mean annual temperature, in the eastern
Sierra slopes of the Owens-Mono watersheds, in 1951-1999. Each of the 49 plotted points corresponds
to a water year. The linear correlation is low (R2=0.1).
9
3. THE 16 GLOBAL CLIMATE MODELS
STUDIED
Table 3-1 lists the sixteen global climate models (GCMs) studied, the corresponding modeling group, and
the primary literature reference for each model.
Table 3-1: The sixteen global climate models
Modeling Group, Country
IPCC Model I.D.
Primary Reference
1.
Bjerknes Centre for Climate Research
BCCR-BCM2.0
(Furevik et al., 2003)
2.
Canadian Centre for Climate Modeling & Analysis
CGCM3.1 (T47)
(Flato and Boer,
2001)
3.
Météo-France / Centre National de Recherches Météorologiques, France
CNRM-CM3
(Salas-Mélia et al.,
2005)
4.
CSIRO Atmospheric Research, Australia
CSIRO-Mk3.0
(Gordon et al., 2002)
5.
US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory, USA
GFDL-CM2.0
(Delworth et al.,
2006)
6.
US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory, USA
GFDL-CM2.1
(Delworth et al.,
2006)
7.
NASA / Goddard Institute for Space Studies, USA
GISS-ER
(Russell et al., 2000)
8.
Institute for Numerical Mathematics, Russia
INM-CM3.0
(Diansky and
Volodin, 2002)
9.
Institut Pierre Simon Laplace, France
IPSL-CM4
(IPSL, 2005)
10.
Center for Climate System Research (The University of Tokyo), National Institute for Environmental
Studies, and Frontier Research Center for Global Change (JAMSTEC), Japan
MIROC3.2
(medres)
(K-1 model
developers, 2004)
11.
Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA
ECHO-G
(Legutke and Voss,
1999)
12.
Max Planck Institute for Meteorology, Germany
ECHAM5/MPIOM
(Jungclaus et al.,
2006)
13.
Meteorological Research Institute, Japan
MRI-CGCM2.3.2
(Yukimoto et al.,
2001)
14.
National Center for Atmospheric Research, USA
PCM
(Washington et al.,
2000)
15.
National Center for Atmospheric Research, USA
CCSM3
(Collins et al., 2006)
16.
Hadley Centre for Climate Prediction and Research / Met Office, UK
UKMO-HadCM3
(Gordon et al., 2000)
A description of the statistically downscaled projected datasets from these 16 models, under emissions
scenarios B1 and A2 is given in Maurer et al. (2007a).
10
4. STATISTICAL DOWNSCALING OF GCM
PROJECTIONS OF TEMPERATURE AND
PRECIPITATION
While large scale (200-500 km) patterns of precipitation and temperature simulated by state-of-the-art
GCMs can be realistic, even the best models display biases on regional scales that are large enough to
confound studies of the hydrologic impacts of climate change. Many different techniques have been
employed to process the raw GCM output to retain the large scale signal of the evolving climate
simulated by the GCM while reproducing historical climate patterns on the landscape at local scales, an
essential characteristic for meaningful hydrologic analysis (Wood et al., 2004).
For this study, we employ the BCSD (Bias Correction and Statistical Downscaling) technique, originally
developed by Wood et al. (2002) for using global model forecast output for long-range streamflow
forecasting. This technique was later adapted for use in studies examining the hydrologic impacts of
climate change (Hayhoe et al., 2004; Maurer and Duffy, 2005; Payne et al., 2004; van Rheenen et al.,
2004). BCSD is an empirical statistical technique in which the monthly precipitation and temperature
output from a GCM are downscaled. The technique uses a quantile-based mapping (Panofsky and Brier,
1968) of the probability density functions for the monthly GCM precipitation and temperature onto those
of grided observed data, spatially aggregated to the GCM scale. This same mapping is applied to future
GCM projections. This allows the mean and variability of a GCM to evolve in accordance with the GCM
simulation, while matching all statistical moments between the GCM and observations for the base
period. This technique has compared favorably to different statistical and dynamic downscaling technique
(Wood et al., 2004; Maurer and Hidalgo, 2008) in the context of hydrologic impact studies. In particular,
the BCSD method, using monthly GCM output as in the current study, was shown to outperform
11
dynamically downscaled RCM output for simulating observed monthly streamflows across the Pacific
Northwest. The method is computationally efficient and has thus been applied to studies downscaling
multiple, extended GCM simulations for hydrologic and other impact studies (Cayan et al., 2009; Hayhoe
et al., 2004; Maurer and Duffy, 2005; Maurer, 2007). Recently, 112 150-year GCM projections were
downscaled over much of North America using the BCSD method (Maurer et al., 2007), demonstrating
its applicability to downscaling large ensembles of GCM simulations.
The BCSD technique assumes that the relationship between the predictor variables (GCM-scale
precipitation and temperature) and the predictands (1/8, or 12 km, precipitation and temperature) is
stationary. However, some of the anticipated changes will be accompanied by non-linear feedbacks,
which cannot be represented in a statistical scheme based on past observations, when the feedbacks did
not exist at a particular location. For example, as warmer temperatures cause the snow line to retreat to
higher elevations, the ground that is exposed has a much lower albedo than the snowpack, thus absorbing
more incident solar radiation. This effect produces higher temperatures in the vicinity of the retreating
snow line, enhancing further melt as a positive feedback. For a region where the observational record
always has snow cover, this sort of effect would not be captured in any statistical relationship.
12
Figure 4-1: Projections by the 16 GCMs for future time horizons, and observations-based historical
values, for period-mean precipitation and temperature. While the individual GCMs are not identified
(given the large number of points), the horizon and emissions scenario is indicated by color.
13
B1
A2
Figure 4-2: Projected rise in monthly temperature for the end of century period (2070-2099) under the
B1 and A2 emissions scenarios, for each GCM and their average. On average, the rise in September
temperatures is the largest, and has the greatest degree of agreement among models.
14
5. VIC MODEL CALIBRATION AND
VERIFICATION
The Variable Infiltration Capacity (VIC) hydrologic model (Liang et al., 1994, 1996; Nijssen et al., 1997,
2001) is a macro-scale hydrologic model that balances both surface energy and water fluxes over a grid
mesh, at resolutions that can range from a fraction of a degree to several degrees latitude by longitude.
One distinguishing characteristic of the VIC model is its use of a subgrid parameterization of the effects
of spatial variability in soils, topography, and vegetation that allows it to represent the observed nonlinear
soil moisture dependence of the partitioning of precipitation into direct runoff and infiltration. The VIC
model also features a nonlinear mechanism for simulating slow (baseflow) runoff response, and explicit
treatment of vegetation effects on the surface energy balance. VIC includes a physically-based
representation of snow dynamics (Cherkauer et al., 2003; Cherkauer and Lettenmaier, 2003).
VIC has been successfully applied to watersheds worldwide for many years (Nijssen et al., 1997; Wood et
al., 1997), and has been extensively used in climate change studies in California and the Western U.S.
(e.g., Cayan et al., 2008; Hayhoe et al., 2004; Maurer and Duffy, 2005; Maurer, 2007; Payne et al., 2004;
van Rheenen et al., 2004).
In our study, the soil characteristics used were taken from gridded 1/8° data sets developed as part of the
Land Data Assimilation Scheme (Mitchell et al., 1999) project, which were in turn derived from the 1-km
resolution data set produced by Pennsylvania State University (Miller and White, 1998) using US
Department of Agriculture State Soil Geographic (STATSGO) Database. Soil parameters, including the
depth of each of the three soil layers, binf, Ds, and Dsmax, were used in model calibration (see
http://www.hydro.washington.edu/Lettenmaier/Models/VIC/Documentation/Calibration.shtml).
Land
cover characterization was based on the University of Maryland global vegetation classifications
described by Hansen et al. (2000), which has a spatial resolution of 1 km, with the hydrologically
15
important characteristic of leaf area index (LAI) derived from Myneni et al. (1997). Details on the uses
and adaptations of these datasets for incorporation in the VIC model are in Maurer et al. (2002).
VIC has been successfully applied from the global scale to river basin scale (e.g., Abdulla et al., 1996;
Maurer et al., 2001, 2002; Nijssen et al. 1997, 2001), for studies of land surface climate teleconnections
(e.g. Zhu et al., 2005), climate change assessments (Payne et al., 2004; van Rheenen et al., 2004;
Christensen et al., 2004; Maurer and Duffy, 2005; Maurer et al., 2007b; Elsner et al., 2010; Hamlet et al.,
2010), and other purposes.
In our study, the VIC model was calibrated separately for each subregion (or “calibration element”) of the
eastern Sierra slopes of Owens Valley and Mono Lake watersheds marked in color in Figure 5-1.
Calibration was based in the observational period 1950-1979 and validation was based in the
observational sub-period 1980-2005. The grid used for meteorological forcings to the model is shown in
Figure 5-2. When all calibration elements are combined, we obtain results for the entire colored area.
These results are shown in Figures 5-3 and Figure 5-4.
In addition to the soil parameters used in calibration (see above), parameters related to snow were
important for our calibrations. The depth of precipitation within a pixel (1.8˚, see Figure 5-2) was
assumed to increase linearly with elevation. The rate of linear increase with elevation was calibrated
separately for each calibration element. The rate of decline in snow albedo in time was also used in
calibration, affecting the energy balance and snowmelt, so as to match the snowmelt hydrograph in each
calibration element.
16
Figure 5-1: Topographic and hydrographic map of the Mono-Owens watersheds showing ten subregions of the watershed contributing the most runoff. These colored regions amount to an estimated
2,118 km2. The gage numbers indicated in the figure are given for location purposes only. The
streamflow values from summary stations representing unimpaired flows were used, rather than
measurements taken at these gages alone. Streamflow data were provided by the Los Angeles
Department of Water and Power and are gratefully acknowledged.
17
Figure 5-2: Map of the Mono-Owens watersheds showing the regular grid used for climatological input
to the VIC hydrologic model. This grid has resolution 1/8° which, at this latitude, corresponds to a grid
cell size of approximately 157 km2.
18
Figure 5-3: Simulated versus observed annual runoff in the simulation area shown in Figure 1, in 19502005. Each of the plotted points corresponds to a year. The correlation is high, R2=0.986.
Figure 5-4: Simulated and observed mean monthly runoff in the simulation area shown in Figure 1, in
1950-2005. In those areas where reliable observed unimpaired streamflows are not available, the
observed streamflows were replaced by the simulated values to create this figure. This includes subregions 2a and 7b as well as portions of sub-regions 4, 5 and 6.
19
Calibration and Verification Periods:
Calibration Period: 1950-1979 (30 years)
Verification Period: 1980-2005 (26 years)
Figure 5-5: Simulated and observed hydrographs (monthly, for 1950-2005), for the entire simulation
area (shown in color in Figure 5-1).
20
Figure 5-6: Simulated snow water equivalent (monthly), 1950-2005, for the entire simulation area.
Figure 5-7: Simulated and observed hydrographs with simulated snow water equivalent superimposed
(monthly, for 1950-2005), for the entire simulation area.
21
Figure 5-8: Cumulative runoff, observed and simulated (monthly, for 1950-2005), for the entire
simulation area.
22
6. RELATIONSHIP BETWEEN MEAN
ANNUAL RUNOFF AND MEAN ANNUAL
PRECIPITATION FOR EACH INDIVIDUAL
GCM
In our manuscript’s Figure 5e we plot the period mean runoff plotted against period mean precipitation,
for all GCMs, with the 30-year period and emissions scenario (B1 or A2) indicated by color; plus the
mean simulations for the 49-year observed period 1951-1999 (in yellow).
In figure 6-1, we decompose that Figure 5e into one plot per GCM. There are now only 7 points in each
plot. We also add (at the top) the plot for the observational period that served as the basis for the
statistical downscaling of the projections (1951-1999). Because the period is only 50 years in length, we
used 10-year (instead of 30-year) time periods – see the top figure panel.
The slope (0.871) and intercept (179) obtained by least-squares linear regression using all data points in
Figure 5e, cannot be rejected for any of the subsets in Figure 6-1.
To provide insight into Figure 6 of the manuscript, in Figure 6-2 we plot results for a single GCM run,
CNRM (A2) for water years 1951-2099 (149 years). In this figure, each gray point corresponds to a
simulated water year. The years composing the wettest decade (the 2020s) are marked in green, and the
years of the driest decade (the 2060s) are in orange. The 30-year year means are plotted in black. There
are only five black points (the number of 30-year periods) thus we also plotted the 10-year means (15
points, in blue).
A year belonging to the wet decade will tend to plot above the regression line of all years, while a year
with a similar precipitation total but that belongs to the dry decade will tend to plot below the regression
23
line. This is explained by antecedent moisture conditions. See also the lower-right inset of Figure 6 in the
manuscript.
In Figure 6-2, the regression slope for the 10-year means is 0.8903, similar to the slope in Figure 5e
(0.8906, rounded to 0.891 in equation 5). The regression slope for the individual years is lower, 0.723.
We calculated the regression slope (and intercept) for the individual years for all model runs, and plotted
the results in Figure 6-3.
24
25
26
Figure 6-1: Simulated period-mean runoff plotted against projected period-mean precipitation for the
historical period (observed and simulated by VIC, top panel) and for each individual GCM for future time
horizons.
27
Figure 6-2: Analogous to Figure 6 of the manuscript, but for a single GCM run, CNRM (A2). See text for
details and interpretation.
28
Scenario B1
Scenario A2
Figure 6-3: Linear regression parameters a and b in y = a∙x – b, where x and y are mean annual
precipitation and runoff, respectively. Figure 6-2 shows details for the case of CNRM (A2). Each color
corresponds to a GCM, as indicated in the legend. Parameter values are plotted for: 1) the entire
simulation period (water years 1951-2099), 2) the 59-year historical period 1951-2009, 3) the 30-year
21st-century period (2010-2039), 4) the 30-year mid-21st century period (2040-2069), and 5) the 30-year
late 21st century period (2070-2099). The range of variability among models is least when the entire
period (1951-2099) is considered because the sample size is larger. The mean of model values for each
time period is shown by the solid black line. The values of a* and b* are indicated by the dashed black
lines are the parameters of Equation 5 which represents the 30-year means rather than annual values,
and are distinctly higher than any values of a and b (respectively) for any of the models in any of the
time periods.
29
LITERATURE REFERENCES
ACPI (2004) The accelerated climate prediction initiative. Clim Change 62:444
Adam JC, Hamlet AF, Lettenmaier DP (2009) Implications of global climate change for snowmelt
hydrology in the twenty-first century. Hydrol Proc 23:962–972. doi: 10.1002/hyp.7201
Aguado E, Cayan D, Riddle L, Roos M (1992) Climatic fluctuations and the timing of west coast
streamflow. J Clim 5:1468-1483
Barnett TP, Pierce DW, Hidalgo HG, Bonfils C, Santer BD, Das T, Gala G, Wood AW, Nozawa T, Mirin
AA, Cayan DR, Dettinger MD (2008) Human-Induced Changes in the Hydrology of the Western United
States. Sciencexpress 10.1126/science.1152538:1-9
Barnett TP, Adam JC, and Lettenmaier DP (2005) Potential impacts of a warming climate on water
availability in snow-dominated regions. Nature 438:303-309
Brown RD (2000) Northern Hemisphere snow cover variability and change, 1915-97. J Clim 13:23392355
Brown TJ, Hall BL, Westerling AL (2004) The impact of twenty-first century climate change on wildland
fire danger in the western United States: an appllications perspective. Clim Change 62:365-388
Cayan DR (1996) Interannual climate variability and snowpack in the western United States. J Climate
9:928-948.
Cayan D, Tyree M, Dettinger M, Hidalgo H, Das T, Maurer EP, Bromirski P, Graham N, Flick R (2009)
Climate change scenarios and sea level rise estimates for the 2008 California climate change scenarios
assessment. California Energy Commission, California Climate Change Center, Sacramento, CA. 64 pp
30
Cayan DR, Maurer EP, Dettinger MD, Tyree M, Hayhoe K (2008) Climate change scenarios for the
California region. Clim Change 87:21-42
Cayan DR, Kammerdiener S, Dettinger M, Caprio J, Peterson D (2001) Changes in the onset of spring in
Western United States. Bull Amer Meteor Soc 82 (3):319-415
Christensen NS, Wood AW, Voisin N, Lettenmaier DP, Palmer RN (2004) Effects of Climate Change on
the Hydrology and Water Resources of the Colorado River Basin, Climatic Change 62 (1-3): 337-363
Collins WD, Bitz CM, Blackmon ML, Bonan GB, Bretherton CS, Carton JA, Chiang P, Doney SC, Hack
JJ, Henderson TB, Keihl JT, Large WG, McKenna DS, Santer BD, Smith RD (2006) The Community
Climate System Model Version 3 (CCSM3). J Clim 19:2122-2143
Costa-Cabral MC, Roy SB, Maurer EP, Mills WB, Chen L (this issue) Snowpack and runoff response to
climate change in Owens Valley and Mono Lake watersheds. Clim Change, this issue
Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA, Cooke WF, Dixon KW, Dunne J,
Dunne KA, Durachta JW, Findell KL, Ginoux P, Gnanadesikan A, Gordon CT, Griffies SM, Gudgel R,
Harrison MJ, Held IM, Hemler RS, Horowitz LW, Klein SA, Knutson TR, Kushner PJ, Langenhorst AR,
Lee H-C, Lin S-J, Lu J, Malyshev SL, Milly PCD, Ramaswamy V, Russell J, Schwarzkopf MD,
Shevliakova E, Sirutis JJ, Spelman MJ, Stern WF, Winton M, Wittenberg AT, Wyman B, Zeng F, Zhang
R (2006) GFDL's CM2 global coupled climate models. Part 1: Formulation and simulation characteristics.
J Climate 19(5):643-674
Dettinger MD, and Cayan, DR (1995) Large-scale atmospheric forcing of recent trends toward early
snowmelt runoff in California. J Climate 8:606-623
31
Dettinger MD, Cayan D, Knowles N, Westerling A, Tyree M (2004b) Recent projections of 21st-century
climate change and watershed responses in the Sierra Nevada. USDA Forest Service, Gen. Tech. Rep.
PSW-GTR-193 pp 43-46
Diansky NA, Volodin EM (2002) Simulation of present-day climate with a coupled Atmosphere-ocean
general circulation model, Izv. Atmos Ocean Phys (Engl. Transl.) 38(6):732-747
Elsner MM, Cuo L, Voisin N, Deems JS, Hamlet AF, Vano JA, Mickelson KEB, Lee SY, Lettenmaier
DP (2010) Implications of 21st Century climate change for the hydrology of Washington State. Climatic
Change 102 (1-2): 225-260. doi:10.1007/s10584-010-9855-0
Ficklin DL, Stewart IT, Maurer EP (this issue) Effects of projected climate change on the hydrology in
the Mono Lake basin, California. Clim Change, this issue
Flato GM, Boer GJ (2001) Warming asymmetry in climate change simulations Geophys Res Lett 28:195198
Furevik T, Bentsen M, Drange H, Kindem IKT, Kvamstø NG (2003): Description and evaluation of the
bergen climate model: ARPEGE coupled with MICOM. Clim Dyn 21(1):27-51
Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The
simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled
model without flux adjustments. Clim Dyn, 16(2-3), 147-168
Gordon HB, Rotstayn LD, McGregor JL, Dix MR, Kowalczyk EA, O’Farrell SP, Waterman LJ, Hirst
AC, Wilson SG, Collier MA, Watterson IG, Elliott TI (2002) The CSIRO Mk3 climate system model.
CSIRO Atmospheric Research Technical Paper 60. CSIRO, Division of Atmospheric Research, Victoria,
Australia
32
Gleick PH (1987) Regional hydrologic consequences of increases in atmospheric CO2 and other trace
gases. Clim Change 19:137-160
Groisman PY, Knight RW, Karl T, Easterling DR, Sun B, Lawrimore JH (2004) Contemporary changes
of the hydrological cycle over the continuous United States, trends derived from in situ observations. J
Hydromet 5:65-84
Hamlet AF, Lee SY, Mickleson KEB, Elsner MM (2010) Effects of projected climate change on energy
supply and demand in the Pacific Northwest and Washington State, Climatic Change, doi:
10.1007/s10584-010-9857-y
Hamlet AF, Mote PW, Clark MP, and Lettenmaier DP (2005) Effects of temperature and precipitation
variability on snowpack trends in the western United States. J Climate 18:4545-4561
Hansen MC, DeFries RS, Townshend RG, Sohlberg R (2000) Global land cover classification at 1 km
spatial resolution using a classification tree approach. Int J Remote Sens 21:1331-1364
Hayhoe K, Cayan D, Field CB, Frumhoff PC, Maurer EP, Miller NL, Moser SC, Schneider SH, Cahill
KH, Cleland EE, Dale L, Drapek R, Hanamann RM, Kalkstein LS, Lenihan J, Lunch CK, Neilson RP,
Sheridan SC, Verville JH (2004) Emissions pathways, climate change, and impacts on California. Proc.
National Academy Sci 101: 12422-12427
Hodgkins GA, Dudley RW (2006) Changes in the timing of winter-spring streamflows in eastern North
America, 1913-2002. Geophys Res Lett 33:L06402, doi:10.1029/2005GL025593
Hodgkins GA, Dudley RW, Huntington TG (2003) Changes in the timing of high flows in New England
over the 20th century. J Hydrol 278:244-252
33
IPSL (2005) The new IPSL climate system model: IPSL-CM4, Institut Pierre Simon Laplace des Sciences
de l’Environnement Global, Paris, France, 73 pp
Jungclaus JH, Botzet M, Haak H, Keenlyside N, Luo J-J, Latif M, Marotzke J, Mikolajewicz U, Roeckner
E (2006) Ocean circulation and tropical variability in the AOGCM ECHAM5/MPI-OM. J Climate
19(16):3952–3972
K-1 model developers (2004) K-1 coupled model (MIROC) description’, K-1 technical report, 1, Center
for Climate System Research, University of Tokyo, Tokyo, Japan, 34 pp
Legutke S, Voss R (1999) The Hamburg atmosphere-ocean coupled circulation model ECHO-G,
Technical report 18, German Climate Computer Centre (DKRZ), Hamburg, Germany, 62 pp
Johnson T, Dozier J, Michaelsen J (1999) Climate change and Sierra Nevada snowpack. In: Tranter M A,
R, Blum E, Jones G, Sharp M, and Williams, M (ed) Interactions Between the Cryosphere, Climate and
Greenhouse Gases. Proc. IUGG 99 Symp. HS2, Birmingham, 1999. IAHS, pp 63-70
Knowles N, Dettinger M, Cayan. D (2006) Trends in snowfall versus rainfall in the western United
States. J Clim 19:4545-4559
Lettenmaier DP, Gan TY (1990) Hydrologic sensitivities of the Sacramento–San Joaquin River Basin,
California, to global warming. Water Resour Res 26:69-86
Liang X, Wood EF, and Lettenmaier DP (1996) Surface soil moisture parameterization of the VIC-2L
model: Evaluation and modifications. Global Planetary Change 13:195-206
López-Moreno JI, García-Ruiz JM (2004) Influence of snow accumulation and snowmelt on streamflow
in the central Spanish Pyrenees. Hydrol Sci 49:787-802
34
Maurer EP (2007) Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California
under two emissions scenarios. Clim Change 82 (3-4):309-325
Maurer EP, Hidalgo HG (2008) Utility of daily vs. monthly large-scale climate data: an intercomparison
of two statistical downscaling methods. Hydrol Earth System Sci 12:551-563
Maurer EP, Brekke L, Pruitt T, Duffy PB (2007a) Fine-resolution climate change projections enhance
regional climate change impact studies. Eos, Transactions of the American Geophysical Union
88(47):504. doi:510.1029/2007EO470006
Maurer EP, Stewart IT, Bonfils C, Duffy PB, Cayan D (2007b) Detection, attribution, and sensitivity of
trends toward earlier streamflow in the Sierra Nevada. J Geophys Res 112:D11118
Maurer EP, Duffy PB (2005) Uncertainty in projections of streamflow changes due to climate change in
California. Geophysical Research Letters 32. doi:10.1029/2004GL021462
Maurer EP, O'Donnell GM, Lettenmaier DP, Roads JO (2001) Evaluation of the land surface water
budget in NCEP/NCAR and NCEP/DOE reanalyses using an off-line hydrologic model. J Geophys Res
106 (D16): 17841-17862, doi: 10.1029/2000JD900828
Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically-based
data set of land surface fluxes and states for the conterminous United States. J Climate 15: 3237-3251
Miller DA, White RA (1998) A conterminous United States multi-layer soil characteristics data set for
regional climate and hydrology modeling. Earth Interactions 2. http://EarthInteractions.org
Myneni RB, Nemani RR, Runninf SW (1997) Estimation of global leaf area index and absorbed PAR
using radiative transfer model. IEEE Trans. Geosci. Remote Sens. 35:1380-1393
35
Mitchell K, and coauthors (1999): GCIP Land Data Assimilation System (LDAS) project now underway.
GEWEX News 9(4):3-6
Moser S, Franco G, Pittiglio S, Chou W, Cayan D (2009) The future is now: an update on climate change
science impacts and response options for California. Special report CEC-500-2008-071 of the California
Climate Change Center
Mote PW, Hamlet AF, Clark M, Lettenmaier D (2003) Trends in snow water equivalent in the Pacific
Northwest and their climatic causes. Geophys Res Lett 30:1601. doi:10.1029/2003GL017258
Mote PW, Hamlet AF, Clark M, Lettenmaier D (2005) Declining mountain snowpack in western north
america. Bull Amer Meteor Soc 86:19-49
Nijssen B, O'Donnell GM, Lettenmaier DP, Lohmann D, Wood EF (2001) Predicting the discharge of
global rivers. J Climate 14:3307-3323
Nijssen B, Lettenmaier DP, Liang X, Wetzel SW, Wood EF (1997) Streamflow simulation for
continental-scale river basins. Water Resour Res 33:711-724
Panofsky HA, Brier GW (1968) Some Applications of Statistics to Meteorology. 224 pp. The
Pennsylvania State University, University Park, PA, USA.
Payne JT, Wood AW, Hamlet AF, Palmer RN, Lettenmaier DP (2004) Mitigating the effects of climate
change on the water resources of the Columbia River basin, Clim Change 62 (1-3):233-256
Pederson GT, Gray ST, Woodhouse CA, Betancour JL, Fagre DB, Little JS, Watson E, Luckman BH,
Graumlich LJ (2011) The unusual nature of recent snowpack declines in the North American Cordillera.
Science 333:332-335
36
Pierce DW, Barnett TP, Hidalgo H, Das T, Bonfils C, Santer B, Bala G, Dettinger M, Cayan D, Mirin A,
Wood A, Nozawa T (2008) Attribution of declining western U.S. snowpack to human effects. J Clim
21:6425-6444
Pupacko A (1993) Variations in northern Sierra Nevada streamflow, Implications of climate change.
Water Resour Bull 29:283-290
Regonda S, Rajagopalan B, Clark M, Pitlick J (2005) Seasonal cycle shifts in hydroclimatology over the
western United States. J Clim 18:372-384
Roos M (1987) Possible changes in California snowmelt patterns. Proc. Fourth Pacific Climate
Workshop, Pacific Grove, CA, PACLIM: 22-31
Roos M (1991) A trend of decreasing snowmelt runoff in Northern California. In: 59th Western Snow
Conf., Juneau, AK, 1987. pp 29-36
Running
S
(2006)
Is
global
warming
causing
more,
larger
wildfires?
Sciencexpress
10.11262/science/113070
Russell GL, Miller JR, Rind D, Ruedy RA, Schmidt GA, Sheth S (2000) Comparison of model and
observed regional temperature changes during the past 40 years. J Geophys Res 105(D11):14891-14898
Salas-Mélia D, Chauvin F, Déqué M, Douville H, Gueremy JF, Marquet P, Planton S, Royer JF, Tyteca S
(2005) Description and validation of the CNRM-CM3 global coupled model, CNRM working note 103,
Centre National de Recherches Météorologiques, Météo-France, Toulouse, France, 36 pp
Stewart I, Cayan DR, Dettinger M (2005) Changes toward earlier streamflow timing across western North
America. J Climate 18:1136-1155
37
van Rheenen NT, Wood AW, Palmer RN, Lettenmaier DP (2004) Potential Implications of PCM Climate
Change Scenarios for Sacramento - San Joaquin River Basin Hydrology and Water Resources, Clim
Change 62 (1-3):257-281
Wahl KL (1992) Evaluation of trends in runoff in the Western United States. In: Herrmann R (ed)
Managing Water Resources during Global Change. American Water Resources Association 28th Annual
Conference & Symposium, Reno, NV, 1992. pp 701-710
Washington WM, Weatherly JW, Meehl GA, Semtner AJ, Bettge TW, Craig AP, Strand WG, Arblaster J,
Wayland VB, James R, and Zhang Y
(2000) Parallel climate model (PCM) control and transient
simulations, Clim Dyn 16(10-11):755-774
Westerling AL, Hidalgo HG, Cayan DR, Swetnam T (2006) Warming and earlier spring increases
western U.S. forest wildfire activity. ScienceExpress 10.1126/science.1128834:1-9
Wolkovich EM, Cook BI, Allen JM, Crimmin TM, Betancourt JL, Travers SE, Pau S, Regetz J, Davies J,
Kraft NJB, Ault TR, Bolmgren K, Mazer SJ, McCabe GJ, McGill BJ, Parmesan C, Salamin N, Schwartz
MD, Cleland EE (2012) Warming experiments underpredict plant phonological responses to climate
change. Nature. Doi:10.1038/nature11014
Wood AW, Lettenmaier DP, Palmer RN (1997) Assessing climate change implications for water
resources planning. Clim Change 37: 203-228
Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic
forecasting for the eastern United States. J Geophys Res 107(D30):4429. Doi:4410.1029/2001JD000659
Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and
statistical approaches to downscaling climate model outputs. Clim Change 62(1-3):189-216
38
Yukimoto S, Noda A, Kitoh A, Sugi M, Kitamura Y, Hosaka M, Shibata K, Maeda S, and Uchiyama T
(2001) The new Meteorological Research Institute coupled GCM (MRI-CGCM2), Model climate and
variability. Papers Meteorol Geophys 51:47-88
Zhu CM, Lettenmaier DP, Cavazos T (2005) Role of antecedent land surface conditions on North
American monsoon rainfall variability. J Climate 18 (16): 3104-3121
39