Richard C. Levine

The role of northern Arabian Sea surface temperature biases in CMIP5 model simulations and future projections of Indian summer monsoon rainfall

Many climate models have problems simulating Indian summer monsoon rainfall and its variability, resulting in considerable uncertainty in future projections. Problems may relate to many factors, such as local effects of the formulation of physical parametrisation schemes, while common model biases that develop elsewhere within the climate system may also be important. Here we examine the extent and impact of cold sea surface temperature (SST) biases developing in the northern Arabian Sea in the CMIP5 multi-model ensemble, where such SST biases are shown to be common. Such biases have previously been shown to reduce monsoon rainfall in the Met Office Unified Model (MetUM) by weakening moisture fluxes incident upon India. The Arabian Sea SST biases in CMIP5 models consistently develop in winter, via strengthening of the winter monsoon circulation, and persist into spring and summer. A clear relationship exists between Arabian Sea cold SST bias and weak monsoon rainfall in CMIP5 models, similar to effects in the MetUM. Part of this effect may also relate to other factors, such as forcing of the early monsoon by spring-time excessive equatorial precipitation. Atmosphere-only future time-slice experiments show that Arabian Sea cold SST biases have potential to weaken future monsoon rainfall increases by limiting moisture flux acceleration through non-linearity of the Clausius–Clapeyron relationship. Analysis of CMIP5 model future scenario simulations suggests that such effects are small compared to other sources of uncertainty, although models with large Arabian Sea cold SST biases may suppress the range of potential outcomes for changes to future early monsoon rainfall.

Dependence of Indian monsoon rainfall on moisture fluxes across the Arabian Sea and the impact of coupled model sea surface temperature biases

The Arabian Sea is an important moisture source for Indian monsoon rainfall. The skill of climate models in simulating the monsoon and its variability varies widely, while Arabian Sea cold sea surface temperature (SST) biases are common in coupled models and may therefore influence the monsoon and its sensitivity to climate change. We examine the relationship between monsoon rainfall, moisture fluxes and Arabian Sea SST in observations and climate model simulations. Observational analysis shows strong monsoons depend on moisture fluxes across the Arabian Sea, however detecting consistent signals with contemporaneous summer SST anomalies is complicated in the observed system by air/sea coupling and large-scale induced variability such as the El Niño-Southern Oscillation feeding back onto the monsoon through development of the Somali Jet. Comparison of HadGEM3 coupled and atmosphere-only configurations suggests coupled model cold SST biases significantly reduce monsoon rainfall. Idealised atmosphere-only experiments show that the weakened monsoon can be mainly attributed to systematic Arabian Sea cold SST biases during summer and their impact on the monsoon-moisture relationship. The impact of large cold SST biases on atmospheric moisture content over the Arabian Sea, and also the subsequent reduced latent heat release over India, dominates over any enhancement in the land-sea temperature gradient and results in changes to the mean state. We hypothesize that a cold base state will result in underestimation of the impact of larger projected Arabian Sea SST changes in future climate, suggesting that Arabian Sea biases should be a clear target for model development.

The resolution sensitivity of the South Asian monsoon and Indo-Pacific in a global 0.35° AGCM

The South Asian monsoon is one of the most significant manifestations of the seasonal cycle. It directly impacts nearly one third of the world’s population and also has substantial global influence. Using 27-year integrations of a high-resolution atmospheric general circulation model (Met Office Unified Model), we study changes in South Asian monsoon precipitation and circulation when horizontal resolution is increased from approximately 200–40 km at the equator (N96–N512, 1.9°–0.35°). The high resolution, integration length and ensemble size of the dataset make this the most extensive dataset used to evaluate the resolution sensitivity of the South Asian monsoon to date. We find a consistent pattern of JJAS precipitation and circulation changes as resolution increases, which include a slight increase in precipitation over peninsular India, changes in Indian and Indochinese orographic rain bands, increasing wind speeds in the Somali Jet, increasing precipitation over the Maritime Continent islands and decreasing precipitation over the northern Maritime Continent seas. To diagnose which resolution-related processes cause these changes, we compare them to published sensitivity experiments that change regional orography and coastlines. Our analysis indicates that improved resolution of the East African Highlands results in the improved representation of the Somali Jet and further suggests that improved resolution of orography over Indochina and the Maritime Continent results in more precipitation over the Maritime Continent islands at the expense of reduced precipitation further north. We also evaluate the resolution sensitivity of monsoon depressions and lows, which contribute more precipitation over northeast India at higher resolution. We conclude that while increasing resolution at these scales does not solve the many monsoon biases that exist in GCMs, it has a number of small, beneficial impacts.

Systematic winter sea-surface temperature biases in the northern Arabian Sea in HiGEM and the CMIP3 models

Analysis of 20th century simulations of the High resolution Global Environment Model (HiGEM) and the Third Coupled Model Intercomparison Project (CMIP3) models shows that most have a cold sea-surface temperature (SST) bias in the northern Arabian Sea during boreal winter. The association between Arabian Sea SST and the South Asian monsoon has been widely studied in observations and models, with winter cold biases known to be detrimental to rainfall simulation during the subsequent monsoon in coupled general circulation models. However, the causes of these SST biases are not well understood. Indeed this is one of the first papers to address causes of the cold biases. The models show anomalously strong north-easterly winter monsoon winds and cold air temperatures in north-west India, Pakistan and beyond. This leads to the anomalous advection of cold, dry air over the Arabian Sea. The cold land region is also associated with an anomalously strong meridional surface temperature gradient during winter, contributing to the enhanced low-level convergence and excessive precipitation over the western equatorial Indian Ocean seen in many models.

On the Structure and Dynamics of Indian Monsoon Depressions

AbstractERA-Interim reanalysis data from the past 35 years have been used with a newly developed feature tracking algorithm to identify Indian monsoon depressions originating in or near the Bay of Bengal. These were then rotated, centralized, and combined to give a fully three-dimensional 106-depression composite structure—a considerably larger sample than any previous detailed study on monsoon depressions and their structure. Many known features of depression structure are confirmed, particularly the existence of a maximum to the southwest of the center in rainfall and other fields and a westward axial tilt in others. Additionally, the depressions are found to have significant asymmetry owing to the presence of the Himalayas, a bimodal midtropospheric potential vorticity core, a separation into thermally cold (~−1.5 K) and neutral (~0 K) cores near the surface with distinct properties, and the center has very large CAPE and very small CIN. Variability as a function of background state has also been explo...

Sensitivity of systematic biases in South Asian summer monsoon simulations to regional climate model domain size and implications for downscaled regional process studies

Global climate models (GCMs) have good skill in simulating climate at the global scale yet they show significant systematic errors at regional scale. For example, many GCMs exhibit significant biases in South Asian summer monsoon (SASM) simulations. Those errors not only limit application of such GCM output in driving regional climate models (RCMs) over these regions but also raise questions on the usefulness of RCMs derived from those GCMs. We focus on process studies where the RCM is driven by realistic lateral boundary conditions from atmospheric re-analysis which prevents remote systematic errors from influencing the regional simulation. In this context it is pertinent to investigate whether RCMs also suffer from similar errors when run over regions where their parent models show large systematic errors. Furthermore, the general sensitivity of the RCM simulation to domain size is informative in understanding remote drivers of systematic errors in the GCM and in choosing a suitable RCM domain that minimizes those errors. We investigate Met Office Unified Model systematic errors in SASM by comparing global and regional model simulations with targeted changes to the domain and forced with atmospheric re-analysis. We show that excluding remote drivers of systematic errors from the direct area of interest allows the application of RCMs for process studies of the SASM, despite the large errors in the parent global model. The findings in this study are also relevant to other models, many of which suffer from a similar pattern of systematic errors in global model simulations of the SASM.

On the climate model simulation of Indian monsoon low pressure systems and the effect of remote disturbances and systematic biases

Monsoon low pressure systems (LPS) are synoptic-scale systems forming over the Indian monsoon trough region, contributing substantially to seasonal mean summer monsoon rainfall there. Many current global climate models (GCMs), including the Met Office Unified Model (MetUM), show deficient rainfall in this region, much of which has previously been attributed to remote systematic biases such as excessive equatorial Indian Ocean (EIO) convection, while also substantially under-representing LPS and associated rainfall as they travel westwards across India. Here the sources and sensitivities of LPS to local, remote and short-timescale forcing are examined, in order to understand the poor representation in GCMs. An LPS tracking method is presented using TRACK feature tracking software for comparison between re-analysis data-sets, MetUM GCM and regional climate model (RCM) simulations. RCM simulations, at similar horizontal resolution to the GCM and forced with re-analysis data at the lateral boundaries, are carried out with different domains to examine the effects of remote biases. The results suggest that remote biases contribute significantly to the poor simulation of LPS in the GCM. As these remote systematic biases are common amongst many current GCMs, it is likely that GCMs are intrinsically capable of representing LPS, even at relatively low resolution. The main problem areas are time-mean excessive EIO convection and poor representation of precursor disturbances transmitted from the Western Pacific. The important contribution of the latter is established using RCM simulations forced by climatological 6-hourly lateral boundary conditions, which also highlight the role of LPS in moving rainfall from steep orography towards Central India.