A wavelet transform method to determine monsoon onset and retreat from precipitation time‐series
International Journal of Climatology Wiley 41:11 (2021) 5295-5317
Abstract:A new method to determine monsoon onset and retreat timings using wavelet transform methodology applied to precipitation time‐series at the pentad scale is described. The principal advantage of this method is its portability, since it can be easily adapted for any region and dataset. The application of the method is illustrated for the North American Monsoon and the Indian Monsoon using four different precipitation datasets and climate model output. The method is shown to be robust across all the datasets and both monsoon regions. The mean onset and retreat dates agree well with previous methods. Spatial distributions of the precipitation and circulation anomalies identified around the onset and retreat dates are also consistent with previous work and illustrate that this method may be used at the grid‐box scale, not just over large area‐averaged regions. The method is also used to characterise the strength and timing of the Midsummer drought in southern Mexico and Central America. A two peak structure is found to be a robust structure in only in 33% of the years, with other years showing only one peak or no signs of a bimodal distribution. The two‐peak structure analysed at the grid‐box scale is shown to be a significant signal in several regions of Central America and southern Mexico. The methodology is also applied to climate model output from the Met Office Hadley Centre UKESM1 and HadGEM3 CMIP6 experiments. The modelled onset and retreat dates agree well with observations in the North American Monsoon but not in the Indian Monsoon. The start and end of the modelled Midsummer drought in southern Mexico and Central America is delayed by one pentad and has a stronger bimodal signal than observed.
The American monsoon system in HadGEM3 and UKESM1
Weather and Climate Dynamics Copernicus Publications 1:2 (2020) 349-371
Abstract:The simulated climate of the American monsoon system (AMS) in the UK models HadGEM3 GC3.1 (GC3) and the Earth system model UKESM1 is assessed and compared to observations and reanalysis. We evaluate the pre-industrial control, AMIP and historical experiments of UKESM1 and two configurations of GC3: a low (1.875∘×1.25∘) and a medium (0.83∘×0.56∘) resolution. The simulations show a good representation of the seasonal cycle of temperature in monsoon regions, although the historical experiments overestimate the observed summer temperature in the Amazon, Mexico and Central America by more than 1.5 K. The seasonal cycle of rainfall and general characteristics of the North American monsoon of all the simulations agree well with observations and reanalysis, showing a notable improvement from previous versions of the HadGEM model. The models reasonably simulate the bimodal regime of precipitation in southern Mexico, Central America and the Caribbean known as the midsummer drought, although with a stronger-than-observed difference between the two peaks of precipitation and the dry period. Austral summer biases in the modelled Atlantic Intertropical Convergence Zone (ITCZ), cloud cover and regional temperature patterns are significant and influence the simulated regional rainfall in the South American monsoon. These biases lead to an overestimation of precipitation in southeastern Brazil and an underestimation of precipitation in the Amazon. The precipitation biases over the Amazon and southeastern Brazil are greatly reduced in the AMIP simulations, highlighting that the Atlantic sea surface temperatures are key for representing precipitation in the South American monsoon. El Niño–Southern Oscillation (ENSO) teleconnections, of precipitation and temperature, to the AMS are reasonably simulated by all the experiments. The precipitation responses to the positive and negative phase of ENSO in subtropical America are linear in both pre-industrial and historical experiments. Overall, the biases in UKESM1 and the low-resolution configuration of GC3 are very similar for precipitation, ITCZ and Walker circulation; i.e. the inclusion of Earth system processes appears to make no significant difference for the representation of the AMS rainfall. In contrast, the medium-resolution HadGEM3 N216 simulation outperforms the low-resolution simulations due to improved SSTs and circulation.
Revisiting gradient wind balance in tropical cyclones using dropsonde observations
Quarterly Journal of the Royal Meteorological Society Wiley 147:735 (2020) 801-824
Abstract:This study diagnoses the degree of gradient wind balance (GWB) in dropsonde observations of 30 tropical cyclones (TCs) divided into 91 intense observation periods. The diagnosed GWB in these observation periods are composited to investigate which characteristics of a TC are significantly related to departures from GWB. This analysis confirms that on average the flow above the boundary layer is approximately in GWB. Supergradient flow is more common near the radius of maximum wind (RMW) in the upper boundary layer than above in the free troposphere or outside the RMW and is also more common in strong storms than in weak storms. In contrast, the degree of GWB does not differ between intensifying, steady‐state and weakening storms. Storms with a peaked wind profile have a higher probability of showing supergradient winds than those with a flat wind profile. The comparison of two commonly used functions to fit observations shows that the diagnosing GWB from dropsonde observations is highly dependent on the analysis technique. The agradient wind magnitude and even sign is shown to depend on which of these functions is used to fit the observations. The use of a polynomial fit consistently diagnoses the presence of supergradient winds far more frequently than a piece‐wise function, and also shows a marked degree of imbalance above the boundary layer. Therefore, caution is warranted when determining the degree of GWB with a polynomial fit.
Air quality in Mexico city during the fuel shortage of January 2019
Atmospheric Environment Elsevier 222 (2019) 117131