Improved Seasonal Prediction of Temperature and Precipitation over Land in a High-Resolution GFDL Climate Model

Abstract

This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lowerresolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

Impacts of different ENSO flavors and tropical Pacific convection variability (ITCZ, SPCZ) on austral summer rainfall in South America, with a focus on Peru

“…This feature is also seen in the satellite-only TRMM 3B43 data set ( , Huffman et al, 2007not shown) and is qualitatively consistent with realistic high-resolution climate models from the Geophysical Fluid Dynamics Laboratory (e.g. GFDL-CM2.5), which show this feature in the western Amazon and all along the eastern tropical Andes (Jia et al, 2015).…”

Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change

“…We assume that the FLOR-FA and HiFLOR modeled responses to changes in radiative forcing are meaningful estimates of the sensitivity of precipitation extremes in the real climate system, since these models capture multiple physical factors affecting precipitation extremes in a physically based and internally consistent framework. This assumption is motivated in part because of the ability of these models to simulate large-scale precipitation and temperature over land (e.g., Van der Wiel et al, 2016;Delworth et al, 2015;Jia et al, 2015Jia et al, , 2016, precipitation extremes over the US (Van der Wiel et al, 2016), modes of climate variability (e.g., Vecchi et al, 2014;Murakami et al, 2015), the meteorological phenomena that led to precipitation extremes and their relationship to modes of climate variability (e.g., Vecchi et al, 2014;Krishnamurthy et al, 2015;Zhang et al, 2015Zhang et al, , 2016Pascale et al, Hydrol. Earth Syst.…”

Explaining Extreme Events of 2014 from a Climate Perspective

“…We performed a preliminary investigation of the dependence of distance on the effect of anthropogenic forcing and natural variability on TC frequency near Hawaii, which revealed that the dependence is small qualitatively. To assess the ability of FLOR to predict the TCs near Hawaii, we first analyzed a retrospective seasonal forecast made using FLOR initialized on 1 July for each year of 1980-2014 (Vecchi et al 2014;Jia et al 2015; see online supplemental material). Figure 23.2a shows the time series of TC number predicted by FLOR, which reasonably predicts the interannual variations of observed TC frequency (r = 0.59).…”