Climate and Hydrologic Change for the North Pacific Rim

Project Overview

Assessing the vulnerability of terrestrial and near-shore ecosystems to climate change presents resource managers with many challenges. In order to determine the extent and specific nature of impacts to these systems, natural resource agencies need internally consistent datasets of fine-scale climatic variables over large regions. An effective way to address the uncertainty of climate model projections is to produce datasets over a range of emissions scenarios from multiple climate models. This project was designed to meet the data needs of natural resource managers and agencies in the North Pacific Region, particularly in terms of impacts to salmon habitat.

The objective of this project was to develop a climate change dataset that covers all watersheds that drain into the north Pacific ocean, from California, through the Bering Straight, to Japan (for a map of the study domain, see Figure 1). The dataset is produced at 0.5 degree resolution (~55 km2) and combines climate projections from the Intergovernmental Panel on Climate Change (IPCC) with hydrological simulations using the Variable Infiltration Capacity (VIC) hydrologic model.

click image to enlarge

Pacific Rim Domain

Figure 1 The domain selected for this study (medium gray), which includes all watersheds that drain into the north Pacific Ocean, defined as the portion of the basin north of 32N. Four calibration watersheds are also highlighted (light gray): the Yukon, Skeena, Fraser, and Sacramento river watersheds. These were used to calibrate the hydrologic model.


We developed a gridded daily historical (1948-2000) dataset following the methods of Njissen et al. (2001), using the monthly CRU TS v2.0 (Climate Research Unit. Jones et al., 1994) as a basis. Corrections were applied to the data for precipitation undercatch, based on streamflow comparisons, and comparisons with regional gridded climate datasets in Alaska (SNAP, 2011), British Columbia (Werner, 2011), the Pacific Northwest (Elsner et al., 2010) and the coterminous U.S. (Daly et al., 2002).

Projections of future climate were obtained from global climate model (GCM) simulations archived as part of the IPCC AR4 assessment, including results for both the A1b and B1 emissions scenarios (Nakicenovic et al. 2000). Six GCMs were chosen for use in the present study:

Data from these GCMs were statistically downscaled to the output 0.5 degree resolution using two methods of statistical downscaling: the Spatial Delta (SD) and Bias Correction and Statistical Downscaling (BCSD) methods of downscaling (both are described in detail by Hamlet et al., 2010). The SD method simply applies the mean monthly changes in temperature and precipitation to the historical record to obtain a perturbed climate time series. The BCSD method, in contrast, uses the full monthly GCM time series (1950-1999) to produce a bias-corrected transient daily time series for each model projection and scenario. In this project we applied the SD method to the mean of all 6 models for three future time periods (2020s, 2040s, and 2080s), while the BCSD approach was applied separately to each model.

Using the Variable Infiltration Capacity (VIC) hydrologic model, the above downscaled projections were used to calculate the implications for hydrology (e.g., snow water equivalent, soil moisture, evapotranspiration, runoff).

The impetus for creating this dataset was to investigate impacts to terrestrial salmon habitat using a 0.125 degree resolution stream temperature model. In order to accommodate this need, all of the raw data are provided at 0.125 degree (subsampled from 0.5 degree).

The result is a set of internally consistent downscaled climate and hydrologic projections at 0.5 degree resolution over the entire domain of the North Pacific Rim. The forcing data are available in raw form, subsampled to 0.125 degree, as well as summarized to monthly time scales at the native dataset resolution of 0.5 degree. Monthly summaries of hydrologic products are also archived for download at 0.5 degree.

We are currently preparing a manuscript that details the development of this dataset (Mantua et al., in prep), associated products, and highlights a few example results. In addition, a detailed README file is included in the data repository.

Table 1 List of Global Climate Models (GCMs) used to provide climate projections
Institution Model Name Resolution Acronym
NASA CGCM v3.1 spectral: T47 cgcm3.1_t47
NASA CGCM v3.1 spectral: T63 cgcm3.1_t63
Max Planck Inst. ECHAM v5 spectral: T63 echam5
Hadley Centre HadCM v3 2.75 x 3.75 deg hadcm
NOAA GFDL CM v2.1 2.0 x 2.5 deg gfdl_cm2.1
CSSR/NIES/FRCGC, Japan MIROC v3.2 spectral: T42 miroc_3.2

Accessing the Data

Data products:

Reference data


Funding for this project was provided by a grant from the Gordon and Betty Moore Foundation via the National Center for Ecological Analysis and Synthesis (NCEAS) at the University of California, Santa Barbara, and from the US Forest Service Pacific Northwest Research Station.


Daly, C. et al., 2002: A knowledge-based approach to the statistical mapping of climate. Climate Research, 22 (2), 99-113.

Elsner et al., 2010: Implications for 21st century climate change for the hydrology of Washington state. Climatic Change, 102 (1-2), 225-260.

Hamlet A.F. et al., 2010: Statistical downscaling techniques for global climate model simulations of temperature and precipitation with application to water resources planning studies. Chapter 4 in Final Project Report for the Columbia Basin Climate Change Scenarios Project,

Jones, P.D., 1994: Hemispheric surface air temperature variations: A reanalysis and an update to 1993. J. Climate, 7, 1794-1802.

Mantua N. et al., 2012: Climate and Hydrologic Change for the North Pacific Rim: A new dataset for climate impacts assessment. Manuscript in preparation.

Nakicenovic, N et al. (2000). Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change.

Nijssen, B. et al., 2001: Global retrospective estimation of soil moisture using the variable infiltration capacity land surface model, 1980-93. J. Climate, 14, 1790-1808.

SNAP (2011). Scenarios Network for Alaska and Arctic Planning. Retrieved March, 2011.

Werner, A.T., 2011: BCSD Downscaled Transient Climate Projections for Eight Select GCMs over British Columbia, Canada. Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, 63 pp.,


Questions regarding this dataset can be directed to:

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