POTENTIAL IMPACTS OF CLIMATE CHANGE ON WATER RESOURCES: Accelerated Climate Prediction Initiative (ACPI) Results for the Columbia River Basin Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Presentation to Northwest Power Planning Council December 12, 2002

Outline of this talk Climate variability and change context Prediction and assessment approach Accelerated Climate Prediction Initiative (ACPI) Hydrology and water management implications for Columbia River Basin Conclusions and comparative analysis

1) Climate variability and change context

Humans are altering atmospheric composition Methane has increased 151%, nitrous oxide 17%, also greenhouse gases

The earth is warming -- abruptly What it is: Average surface temperature year by year 3 main features: warming to 1940 cooling 1940-1970 warming since 1970 causes of these are different 1996-1999 were generally warmer than the top dots shown (continuing warming trend)

Natural Climate Influence Human Climate Influence Red: observations of global average temperature Grey: simulations with a climate model (huge computer program, like weather prediction model only run for hundreds of years) Natural influence: volcanoes, solar variations – guesses before ~1970, better since then Human influence: greenhouse gases, sulfate aerosols it is possible to simulate the climate of the last 100 years, and the conclusion is that humans didn’t matter much before 1960 – the early warming and the cooling were largely natural, and the late-century warming was largely human-caused All Climate Influences

The IPCC used a wide range of assumptions about future economic development, from rapid global economic growth relying mostly on fossil fuels to slow growth in which the developed world goes heavily toward a service economy and the developing world is left behind. For this wide range, CO2 doubles between 2050 and 2100. Best guess: 1C by mid-century, 3C by 2100. Note that constant global temperature is not among the projections

Temperature trends in the PNW over the instrumental record Almost every station shows warming (filled circles) Urbanization not a major source of warming BC: 0.5C (0.9F) in coastal region and 1.1C (2F) in CRB

Trends in timing of spring snowmelt (1948-2000) +20d later –20d earlier Courtesy of Mike Dettinger, Iris Stewart, Dan Cayan

Trends in snowpack

This shows 10-year averages of temperature for the whole PNW; 1990’s were the warmest decade of the 20th century in the Northwest (as well as globally), by almost 1 degree. The rate of warming from 1970 to 2000 has been roughly what the CGCM1 simulates. We looked at 8 scenarios produced by several different general circulation models from climate modeling centers around the world. All used the same simple scenario for CO2: 1%/year. The spread here does not include the spread from other greenhouse gas scenarios. Looking ahead to the 2020s and 2040s, the warmest, average, and coolest of these 8 models are shown as red, yellow, and green. The average rate of warming is a bit less than 1 degree per decade. Even the coolest scenario still shows about 2F more warming by the 2040s.

2) Prediction and assessment approach

Global climate simulations, next ~100 yrs Scenarios Performance Measures Downscaling Global climate simulations, next ~100 yrs Delta Precip, Temp Reliability of System Objectives Reservoir Model Hydrologic Model (VIC) DamReleases, Regulated Streamflow Natural Streamflow

Coupled Land-Atmosphere-Ocean General Circulation Model Reservoir Model Hydrology Model

Bias Correction and Downscaling Approach climate model scenario meteorological outputs  hydrologic model inputs snowpack runoff streamflow 2.8 (T42)/0.5 degree resolution monthly total P, avg. T 1/8-1/4 degree resolution daily P, Tmin, Tmax important point(s): the approach attempts to make use of forecast skill from 2 sources: better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models; the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling). Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

Bias Correction from NCDC observations from PCM historical run raw climate scenario bias-corrected climate scenario month m Note: future scenario temperature trend (relative to control run) removed before, and replaced after, bias-correction step.

interpolated to VIC scale Downscaling observed mean fields (1/8-1/4 degree) monthly PCM anomaly (T42) VIC-scale monthly simulation interpolated to VIC scale

Overview of ColSim Reservoir Model Reservoir Operating Policies Reservoir Storage Regulated Streamflow Flood Control Energy Production Irrigation Consumption Streamflow Augmentation Physical System of Dams and Reservoirs Streamflow Time Series

Dam Operations in ColSim Storage Dams Virgin Regulated Run-of-River Dams Flow In=Flow out + Energy H

ColSim Storage Reservoirs + Run of River Reservoirs Releases Depend on: Storage and Inflow Rule Curves (streamflow forecasts) Flood Control Requirements Energy Requirements Minimum Flow Requirements System Flow Requirements Inflow ColSim Consumptive use Inflow Inflow Consumptive use Inflow Inflow Inflow + Inflow Run of River Reservoirs (inflow=outflow + energy) System Checkpoint

3) Accelerated Climate Prediction Initiative (ACPI)

Accelerated Climate Prediction Initiative (ACPI) – NCAR/DOE Parallel Climate Model (PCM) grid over western U.S.

Climate Change Scenarios PCM Simulations (~ 3 degrees lat-long) Historical B06.22 (greenhouse CO2+aerosols forcing) 1870-2000 Climate Control B06.45 (CO2+aerosols at 1995 levels) 1995-2048 Climate Change B06.44 (BAU6, future scenario forcing) 1995-2099 Climate Change B06.46 (BAU6, future scenario forcing) 1995-2099 Climate Change B06.47 (BAU6, future scenario forcing) 1995-2099 PNNL Regional Climate Model (RCM) Simulations (~ ¾ degree lat-long) important point(s): GSM forecasts take the form of monthly ensembles of length 6 months we get them early in each month for a start date of the following month. the climatology ensemble enables us to define the climate model bias and correct it climatology ensembles run out 6 months just like the forecasts, but use observed rather than predicted tropical Pacific SSTs also: 210 ensembles for GSM climatology are derived from observed SSTs in each year of the 21 year climatology period (1979-1999) combined with 10 initial atmospheric conditions for each year GSM is at T42 spatial resolution, but moving to T62 soon (resolution improvement of about 1/3) Climate Control B06.45 derived-subset 1995-2015 Climate Change B06.44 derived-subset 2040-2060

Future streamflows 3 ensembles averaged summarized into 3 periods;

Regional Climate Model (RCM) grid and hydrologic model domains important point(s): the overall forecasting approach involves using forecast model (the global spectral model) T & P output at a coarse timestep & scale as hydrologic model input at a finer timestep and scale. to make a hydrologic forecast, you need a transformation of the forecasts that first overcomes climate model bias and the scale differences, then simulates the water balance. also, GSM is really run at very fine timestep (~5-15 minutes) but only the monthly anomalies are archived for our use. most of the signal is at the monthly scale, however, so this is acceptable.

ACPI: PCM-climate change scenarios, historic simulation v air temperature observations

ACPI: PCM-climate change scenarios, historic simulation v precipitation observations

4) Hydrology and water management implications: Columbia River Basin

PCM Business-as-Usual scenarios Columbia River Basin (Basin Averages) BAU 3-run average historical (1950-99) control (2000-2048) PCM Business-as-Usual scenarios Columbia River Basin (Basin Averages) important point(s): we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.

RCM Business-as-Usual scenarios Columbia River Basin (Basin Averages) PCM BAU B06.44 RCM BAU B06.44 control (2000-2048) historical (1950-99) important point(s): we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.

PCM Business-As-Usual Mean Monthly Hydrographs Columbia River Basin @ The Dalles, OR important point(s): we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs. 1 month 12 1 month 12

CRB Operation Alternative 1 (early refill)

CRB Operation Alternative 2 (reduce flood storage by 20%) 15,000,000 20,000,000 25,000,000 30,000,000 35,000,000 40,000,000 45,000,000 50,000,000 55,000,000 O N D J F M A S End of Month Total System Storage (acre-feet) Max Storage Control Base Climate Change Change (Alt. 2) Dead Pool

CRB Operation Alternative 3 (allocate more storage for fish) All US + Canadian Current Operations

McNary Flow Deficit McNary Flow Reliability

McNary Flow Deficit McNary Flow Reliability

Trade-Offs Early Refill

Trade-Offs for Storage Allocation

Trade-Off Curves for Alternative 3 Period 1

Trade-Off Curves for Alternative 3 Period 2

Trade-Off Curves for Alternative 3 Period 3

5) Summary evaluation and conclusions Period 1 Firm power is reduced by the system’s inability to meet current hydropower demands without compromising other operating goals (especially instream flows). Total hydropower production and revenue is relatively unaffected (occasionally increasing) under the projected climate changes. However, fish flow targets would be difficult to meet under altered climate, and mitigation by altered operation is essentially impossible in late summer. Although the monthly time step used in this study complicates evaluation of changes in flood risk, the current flood rules appear to be overly conservative, and flood control begins to compete with other water management considerations as hydrographs shift from spring towards winter.