The effect of the Madden‐Julian Oscillation on station rainfall and river level in the Fly River system, Papua New Guinea

Abstract

[1] The Madden-Julian oscillation (MJO) is the dominant mode of intraseasonal variability in tropical rainfall on the large scale, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. The Fly River system, Papua New Guinea, is one of the wettest regions on Earth and is at the heart of the MJO envelope. A 16 year time series of daily precipitation at 15 stations along the river system exhibits strong MJO modulation in rainfall. At each station, the difference in rainfall rate between active and suppressed MJO conditions is typically 40% of the station mean. The spread of rainfall between individual MJO events was small enough such that the rainfall distributions between wet and dry phases of the MJO were clearly separated at the catchment level. This implies that successful prediction of the large-scale MJO envelope will have a practical use for forecasting local rainfall. In the steep topography of the New Guinea Highlands, the mean and MJO signal in station precipitation is twice that in the satellite Tropical Rainfall Measuring Mission 3B42HQ product, emphasizing the need for ground-truthing satellite-based precipitation measurements. A clear MJO signal is also present in the river level, which peaks simultaneously with MJO precipitation input in its upper reaches but lags the precipitation by approximately 18 days on the flood plains.

Scale Interactions between the MJO and the Western Maritime Continent

“…The observations show that, on average, easterly winds persist over the entire column in the wet phases (2 and 3) and those immediately preceding them (8 and 1) and become weakly easterly or westerly toward the end of the wet period (phase 5) and into the drier phases (6 and 7). The largest differences are at midlevels (800-500 hPa), which is consistent with equatorial wave dynamics theory (Matthews 2000). The ERAI data and the two RCMs behave in a similar way to the observations, except that the westerly midlevel winds in phases 5 and 6 are weaker than observed.…”

“…It is known to have particular problems over steep topography, where biases have a strong dependence on elevation (Romilly and Gebremichael 2011). In particular, over the steep and high topography of Papua, New Guinea, at the heart of the MC, TRMM rainfall consistently underestimates station rainfall by a factor of 2, both in the climatological mean and in its MJO anomalies (Matthews et al 2013). The TRMM and station rainfall agree over the low-lying coastal plains.…”

“…Peatman et al (2014) hypothesize that this behavior is a consequence of the interplay between the large-scale circulation and mesoscale circulations forced by the islands of the MC. The large-scale circulation and moisture convergence changes preceding the arrival of the MJO, which are forced by large-scale equatorial wave dynamics (e.g., Hendon and Salby 1994;Maloney and Hartmann 1998;Matthews 2000), increase the moisture availability before the active phase of the MJO sets in over the MC. Peatman et al (2014) suggest that the reason rainfall is enhanced at this time over the land but not the ocean is because solar insolation remains high ahead of the active MJO, which maintains the high landsea temperature contrast that drives the main rainproducing mechanism in this region.…”

The diurnal cycle of rainfall over New Guinea in convection-permitting WRF simulations

“…Nonetheless, measurements by TRMM over steep topography should also be treated with caution, when considering the model rainfall biases over elevated terrain. Recently, Matthews et al (2013) reported that TRMM underpredicted rainfall over high-terrain by as much as 50 % when compared to long-term rain-gauge station data in the New Guinea highlands. Additional results by Chen et al (2013) show similar underestimates of intense rainfall over high terrain (Hawaii).…”

Sea-Breeze Dynamics and Convection Initiation: The Influence of Convective Parameterization in Weather and Climate Model Biases

“…Over coastal highland, all three simulations overestimate the total amount of precipitation. Using rain gauge data, Matthews et al (2013) found that TRMM underestimates rainfall in the highlands of Papua New Guinea by a factor of two. The rainfall in EXPLICIT and SCUMULUS is more than twice that estimated by TRMM; thus, it is unlikely that the difference can be explained by uncertainties in the satellite observations alone.…”