The impact of Systematic Biases in Pacific Ocean SSTs on Predictability of the Hydrological Cycle Over North America in Decadal Climate Prediction Studies

The World Climate Research Program’s Working Group on Coupled Modeling will be carrying out a coordinated set of model experiments that includes, for the first time, simulations of decadal climate prediction. The ultimate goal of these simulations will be to provide policymakers with information on decadal timescales to assess possible consequences of climate change. To what extent these experiments will be useful to stakeholders and policymakers will depend upon whether there is a predictable signal of climate change and to what extent this signal varies on regional scales. In this proposed research we will focus on systematic errors in the predictable signal forced by sea surface temperature (SST) biases in the coupled model’s response to external forcing. In addition, we will investigate how these model biases limit predictability by impacting the spatial and temporal structure of natural variability. An active hypothesis is that the predictable signal of climate change comes from low-frequency ocean variability and it’s forcing of the atmosphere. We will explore this hypothesis by studying how systematic biases in Pacific Ocean SSTs impact the decadal predictability of the hydrological cycle over North America, focused primarily on the following two questions: 

1. To what extent is the predictable decadal signal over North America related to the spatial pattern of SST anomalies in the Indian and Pacific Ocean basins? 

2. Do systematic biases in Indian and Pacific Ocean SSTs impact potential predictability over North America by forcing regional variations in the climate signal, as well as, biases in the spatial and temporal structure of natural variability? 

Based on the results of previous studies, we will use model output from coupled climate model simulations of the 20th Century as unassimilated decadal climate predictions. We will then use AGCM model studies forced by SSTs output from these simulations to determine how biases in the models’ response to radiative forcing (through lowfrequency ocean variability) impact the decadal predictability of the hydrological cycle over North America. We will focus on identifying physical mechanisms that cause biases in predictability over North America, such as biases in the structure of the PDO. We will study the decadal predictability of the hydrological cycle by focusing our analysis on the variability of rainfall, surface temperature, and circulation patterns over North America. We will investigate strategies to correct for model biases in SSTs thereby improving probabilistic projections of decadal climate forecasts.