The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part II: using AMIP runs to understand the heat flux feedback mechanisms

Abstract

Several studies using ocean–atmosphere general circulation models (GCMs) suggest that the atmospheric component plays a dominant role in the modelled El Niño-Southern Oscillation (ENSO). To help elucidate these findings, the two main atmosphere feedbacks relevant to ENSO, the Bjerknes positive feedback (μ) and the heat flux negative feedback (α), are here analysed in nine AMIP runs of the CMIP3 multimodel dataset. We find that these models generally have improved feedbacks compared to the coupled runs which were analysed in part I of this study. The Bjerknes feedback, μ, is increased in most AMIP runs compared to the coupled run counterparts, and exhibits both positive and negative biases with respect to ERA40. As in the coupled runs, the shortwave and latent heat flux feedbacks are the two dominant components of α in the AMIP runs. We investigate the mechanisms behind these two important feedbacks, in particular focusing on the strong 1997–1998 El Niño. Biases in the shortwave flux feedback, α _SW, are the main source of model uncertainty in α. Most models do not successfully represent the negative α_SW in the East Pacific, primarily due to an overly strong low-cloud positive feedback in the far eastern Pacific. Biases in the cloud response to dynamical changes dominate the modelled α _SW biases, though errors in the large-scale circulation response to sea surface temperature (SST) forcing also play a role. Analysis of the cloud radiative forcing in the East Pacific reveals model biases in low cloud amount and optical thickness which may affect α _SW. We further show that the negative latent heat flux feedback, α _LH, exhibits less diversity than α _SW and is primarily driven by variations in the near-surface specific humidity difference. However, biases in both the near-surface wind speed and humidity response to SST forcing can explain the inter-model α_LH differences.

Appendix: Computing U and \({\Updelta}q\)

In order to investigate the α_LH mechanism we require datasets of the near-surface wind speed, U (usually defined at 10 m), and both the near-surface (q _2m ) and surface saturation (q*(T _s)) specific humidities.

ERA40 and eight of the AMIP runs (all except IAP) supply the monthly mean zonal (u ₁₀) and meridional (v ₁₀) wind speeds at 10 m, so we approximate the monthly mean 10 m wind speed consistently for all datasets using: $U = \sqrt{(u_{10})^2+(v_{10})^2}$

For the q _2m observations, we use the OAFlux 2 m specific humidity. The source of this field is the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) 10 m air humidity (Chou et al. 2001), height-adjusted to 2 m before being assimilated in the OAFlux synthesis. Six of the AMIP runs (all models except GFDL2.1, IAP and MPI) also supply the 2 m specific humidity.

In order to calculate the near-surface humidity difference, $\Updelta q$, we also require the surface saturation specific humidity, q*(T _s). First, we calculate the saturation water vapour pressure, e*, at the sea surface temperature using the Goff–Gratch equation:

$$ \begin{aligned} \log_{10}(e^*) &= -7.90298(T_{\text{st}}/T -1) + 5.02808\log_{10}(T_{\text{st}}/T) \\ &\quad- 1.3816 \times 10^{-7}\left(10^{11.344(1-T/T_{\text{st}})} - 1\right) \\ &\quad+ 8.1328 \times 10^{-3}\left(10^{-3.49149(T_{\text{st}}/T-1} -1\right) + \log_{10}(e^*_{\text{st}}) \end{aligned} $$

(8)

where e* is the saturation water vapour pressure (hPa), T is the absolute sea surface temperature (K), T _st is the steam-point temperature (373.15 K) and e ^*_st is $e^*$ at the steam-point pressure (1 atm = 1013.25 hPa).

There is a large number of empirical equations used to calculate the saturation water vapour pressure, but the Goff–Gratch equation [originally Goff and Gratch (1946), modified by Goff (1957)] was recommended for use by the World Meteorological Organization (1988).

The surface saturation specific humidity can then be calculated as follows (Ambaum 2010):

$$ q^*(T_{\text{s}}) = \frac{\epsilon \times e^*}{p-(1-\epsilon)e^*} $$

(9)

where $\epsilon=\mu_v/\mu_d=0.622$, μ _v and μ _d are the effective molar masses of water and dry air respectively, and p is the atmospheric pressure at the sea surface (hPa).

In order to obtain a reference ‘observational’ dataset for $\Updelta q$, we combine the q*(T _s) calculated from the ERA40 SST and sea level pressure fields with the OAFlux q _2m .