Search

Using reanalysis data and observations, interannual variations and long-term trends in the Somali Jet (hereafter, the Jet) and Indian Summer Monsoon Rainfall (ISMR) are characterized for drought vs flood, El Nino vs La Nina, and positive vs negative Indian Ocean Dipole (IOD) summer monsoon seasons. In flood years, the Jet is stronger over the upstream sector (i.e., Arabian Sea) and weaker over the downstream (i.e., Bay of Bengal), resulting in additional convergence and enhanced moisture transport over India, whereas the reverse occurs during drought years. The Jet and ISMR characteristics for El Nino (La Nina) years are similar to drought (flood) years, despite only around half of dry (wet) years, being associated with El Nino (La Nina). In +IOD years, the Jet shifts northward and intensifies over central and northern India, bringing more moisture and rainfall to those regions, while the −IOD has an opposite but relatively weak impact on ISMR. Over the last century, the Jet has become weaker over peninsular India, with reductions in rainfall across east India. Future projections under the SSP585 scenario from the CMIP6 multi-model mean suggest that the Jet will progressively shift northward and strengthen over the Arabian Sea, with associated rainfall increases over entire Indian region. The long-term changes in the Jet over the twentieth century are of comparable magnitude to interannual variations; however, the absolute magnitude of projected future changes is smaller than interannual variations observed in the past.

Recently released NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data (surface air temperature and precipitation) are evaluated using the observations from the India Meteorological Department (IMD) and compared with CMIP5 and CORDEX data to show its advantage over the Indian subcontinent for the summer monsoon season. The multi-model mean of 21 NEX-GDDP models, 28 CMIP5 models, and 10 CORDEX models are analyzed for the period of 1975–2005. In general and over most parts of the subcontinent, the NEX-GDDP is found to be quite satisfactory and surpasses the CMIP5 and CORDEX data. The NEX-GDDP data captures the spatial patterns of seasonal mean temperatures and precipitation with highest accuracy (pattern correlation of ~0.8) and least errors (root mean square error of ~ 4.25 °C and ~2.48 mm day−1); the inter-annual variations in precipitation are closer to the observations (r = 0.66 and standard deviation = 0.36 mm day−1); bias in the annual cycle reduces; simulation of extremes is more realistic with less inter-model differences. Remarkable improvements in this data suggest its potential application for future projections and climate change impact studies at the regional scale.

The Himalaya–Tibetan Highland (HTH) is highly vulnerable to climate change for multiple reasons. In this work, we present past and future changes in HTH climate, using temperature and precipitation from APHRODITE, CMIP5 and NEX-GDDP. To assess observed climate change, we analysed APHRODITE and found significant warming (up to 3 °C) during all seasons but no significant change in precipitation. We validated CMIP5 and NEX-GDDP against APHRODITE and found the latter more accurate. Future climate projections under RCP8.5 using NEX-GDDP suggest widespread warming (~5–8 °C) and increase in monsoon and post-monsoon precipitation (up to ~50%) over HTH by the end of the twenty-first century.

In this paper, the fidelity of 28 models under Coupled Model Inter-comparison Project Phase-5 is examined for the Indian summer monsoon for the historical period from 1975 to 2005. It is found that all models simulate the spatial distribution of the seasonal mean surface air temperatures (Tas) quite well (pattern correlation > 0.75), whereas the simulation of precipitation is found to be relatively poor (correlation 0.1–0.7). Most models underestimate the Tas with more bias during winter and less bias during summer. In regard to precipitation, most models fail to capture the observed contribution ratio of convective and large-scale precipitation (LSP) and simulate more convective precipitation as compared to the LSP. Extremely large wet (dry) biases are noted in convective (large-scale) precipitation. The total precipitation is also noted to have a large dry bias in most models, which is mainly due to the large dry bias in the LSP. Contrary to the notion that better simulation of the contribution ratio would lead to better simulation of total precipitation or vice-versa, our results show that both of these notions are not valid for most models. In observations, the LSP dominates the annual cycle of the total precipitation, whereas in models, the convective component dominates. In few models, the annual cycle in the individual precipitation component is either weak or completely missing. None of the models are found to simulate the observed trend in precipitation and temperature. The model with the highest resolution, MIROC-4h, simulates many of the observed features better than the other models, thereby emphasizing the usefulness of finer resolutions in better simulation of Indian monsoon. A comprehensive list of models has been prepared on the basis of their capability in simulating various features of Indian summer monsoon. The multimodel mean of the better models identified in this study is expected to produce more reliable projections of the Indian monsoon.

Considering the wide use of the multi-model mean (MMM) on the seasonal time scale, this work examines its fidelity in simulating some important characteristics of the Indian summer monsoon using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. It is noted that the MMM captures the observed spatial pattern and annual cycle of surface air temperature to a great extent, but there are large biases in magnitude, particularly over north India. For precipitation, only the broad-scale features are captured and extreme large biases, of magnitude equal or higher than the seasonal mean precipitation, exist in the MMM. The simulation of trends in seasonal mean temperatures and precipitation is even less satisfactory than the climatological means. Several precipitation features, for example, low-to-moderate intensity precipitation events, orography-related rain bands, extreme events, are noted to improve with increasing resolution of the models, whereas, no such improvement is noted for temperatures. It is also noted that the improvement in CMIP5 MMM is marginal if compared with the best performing model from the group of models considered for analysis. There are several models that show similar skill as MMM, and therefore could be alternatively used for future projections. Moreover, using such individual models for Indian monsoon projections will also help us to understand the underlying mechanisms and processes by conducting targeted numerical experiments, which would otherwise be highly limited by approaches like MMM. Therefore, targeted efforts to improve some of these better models are required to gain more confidence in future projections of Indian monsoon. Averaging output from various climate models (the multi-model mean, MMM) fails to capture key characteristics of the Indian monsoon. The MMM—which reduces bias associated with individual models—has been used extensively to investigate observed and projected changes in monsoon rainfall, upon which millions of people rely for water resources. Saroj Mishra and colleagues from the Indian Institute of Technology Delhi use the CMIP5 ensemble to test whether the MMM is able to represent broad-scale monsoon features. Several biases exist in the simulation of both temperature and precipitation. In particular, the magnitude of MMM rainfall is incorrect, low-to-moderate precipitation events are overestimated, while extreme events are underestimated. Thus, care must be taken when using the MMM to interpret projected changes in monsoon variability, and in some instances, analysis of individual models may be preferable.

In this paper, the performance of 28 CMIP5 models in simulating the climate of the Himalaya-Tibetan Plateau (HTP) for the recent past (1975–2005) is evaluated using the observations from the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE). Many models realistically simulate the spatial distribution of surface air temperature (Tas) and precipitation with pattern correlation as high as 0.8; however, they possess severe biases in their magnitude. The biases in Tas appear to be associated with the biases in the surface elevation. All the models capture the observed phase of the annual cycle of the Tas but underestimate the amplitude. For precipitation, the phase is captured by most models (except few), but the amplitude is overestimated in all models. In the mid-intensity precipitation range (10–80 mm day−1), most of the models overestimate the probability of occurrence and show large intermodel differences. Most of the models fail to simulate the spatial distribution of the trend in Tas and precipitation. As compared to many individual models, the biases are noted to reduce when using multimodel means (MMMs); however, the MMMs also failed to capture the observed trends in both Tas and precipitation. Many models still struggle to capture the large-scale phenomena, such as the location and intensity of upper-level Asian anticyclone and middle troposphere temperature maximum over the HTP, which have large implications on the HTP as well as the Indian summer monsoon. The results show that none of the models capture all features of the HTP monsoon, and hence, further improvement in the parameterization schemes and resolution is required to gain more confidence in the projection of HTP climate using these models.

As Himalaya-Tibet Highland (HTH), a spatially extensive and complex terrain, plays influential roles in the regional and global climate, its representation in climate models is a decisive factor in climate simulations. It is established that higher spatial resolution improves the simulation of several seasonal mean features over this region, but how much improvement comes from the better representation of surface topography and how much comes from the better modeling of the atmospheric and surface processes are still not clear. To understand this, three sets of 6-member ensemble simulations are conducted using the NCAR Community Atmosphere Model version 5.1 (CAM5.1) at: (1) 1.9° × 2.5° resolution (Coarse), (2) 0.47° × 0.63° resolution (Fine), and (3) topography is prescribed at the Coarse resolution and rest of the model processes are computed at the Fine resolution (Hybrid). The Coarse resolution overlooks most of the intricate features of the topography with a severe bias of ~ 1–2 km but Fine resolution does a much better job in their representations. Surface air temperatures are found to be strongly dependent on the resolution of topography and therefore it is desirable to resolve topography in models for the realistic simulation and projection of temperatures. Rest of the variables, viz. seasonal mean, seasonal cycle and probability distribution of precipitation; tropospheric temperature and moisture; Tibetan anticyclone, show remarkable improvements with the increase in resolution of the atmospheric and surface processes. Influence of the resolution of topography is found to be limited to the model levels adjacent to the surface, and for higher model levels, the resolution of the atmospheric and surface processes is noted to play a more crucial role.

A new bias-corrected statistically downscaled product, namely, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), has recently been developed by NASA to help the scientific community in climate change impact studies at local to regional scale. In this work, the product is validated over India and its added value as compared to its CMIP5 counterpart for the NCAR CCSM4 model is analyzed, followed by climate change projections under the RCP8.5 global warming scenario using the two datasets for the variables daily maximum 2-m air temperature (Tmax), daily minimum 2-m air temperature (Tmin), and rainfall. It is found that, overall, the CCSM4-NEX-GDDP significantly reduces many of the biases in CCSM4-CMIP5 for the historical simulations; however, some biases such as the significant overestimation in the frequency of occurrence in the lower tail of the Tmax and Tmin still remain. In regard to rainfall, an important value addition in CCSM4-NEX-GDDP is the alleviation of the significant underestimation of rainfall extremes found in CCSM4-CMIP5. The projected Tmax from CCSM4-NEX-GDDP are in general higher than that projected by CCSM4-CMIP5, suggesting that the risks of heat waves and very hot days could be higher than that projected by the latter. CCSM4-NEX-GDDP projects the frequency of occurrence of the upper extreme values of historical Tmax to increase by a factor of 100 towards the end of century (as opposed to a factor of 10 increase projected by CCSM4-CMIP5). In regard to rainfall, both CCSM4-CMIP5 and CCSM4-NEX-GDDP project an increase in annual rainfall over India under the RCP8.5 global warming scenario progressively from the near term through the far term. However, CCSM4-NEX-GDDP consistently projects a higher magnitude of increase and over a larger area as compared to that projected by CCSM4-CMIP5. Projected daily rainfall distributions from CCSM4-CMIP5 and CCSM4-NEX-GDDP suggest the occurrence of events that have no historical precedents. Worth noting is that the extreme daily rainfall values projected by CCSM4-NEX-GDDP are two to three times larger than that projected by CCSM4-CMIP5.

It has been reported that the occurrence of "Heat wave" conditions is increasing in different parts of the world. This increase in heat extremes is expected to lead to increasing levels of discomfort to the human beings. In addition, there are loss of working hours, adverse health impacts and deaths depending on the severity and duration of the heat extremes. It is well known that meteorologically it is not merely the rise in temperature which causes human discomfort. Temperature coupled with high humidity, low wind and impervious clothing form major causes of discomfort. Above all, the type of physical activities being undertaken in these adverse conditions is one of the important factors to decide the stress and strain encountered by a person. Studies on biometeorology have been carried out by several scientists for over a century and a number of heat indices have been developed which quantify the level of discomfort or heat stress in their working environments. Today, observed meteorological parameters as well as corresponding model outputs are freely available from several sources which can be used for estimating the values of heat indices at any location. Both dynamical and statistical downscaling methods are very useful in this regard. In this study, some of the commonly used heat indices have been calculated for the four major cities of Delhi, Mumbai, Chennai and Kolkata using daily observed parameters from the India Meteorological Department (IMD) for the period 1975 to 2005. Same indices are also calculated based on the simulated values from the Regional Climate Model (RegCM) of the Abdus Salam International Centre for Theoretical Physics (ICTP) over the same period. The RegCM simulated fields have been obtained from the COordinated Regional Downscaling EXperiments (CORDEX) over the South Asia domain. Comparison of characteristics of the four selected indices based on IMD observed data and RegCM simulations leads to the inference that there are several similarities between the two sets of data in terms of their annual cycles and inter-annual variations. This exercise conclusively shows the advantages of dynamical downscaling. Further, results of this study encourage for a comprehensive work in the future for the country wide mapping and projection of heat indices based on model simulations, development of suitable heat indices and classification of comfort classes for their use in warning system for human health related issues in India.

Variations of energy fluxes with ENSO, IOD, and Intra-Seasonal Variability of Indian Summer Monsoon Rainfall (ISMR) over the Indian monsoon region are examined using ECMWF Era Interim reanalysis over a period of 1979–2013. The composite anomalies of radiative fluxes, viz. shortwave and longwave, latent, and sensible heat as well as precipitation and winds are analyzed for the years associated with El Nino, La Nina, positive and negative IOD phases, active, and break phases of ISMR with respect to the normal years. It is observed that, the lesser (more) precipitation over the region in the El Nino (La Nina) years is accompanied with enhanced (reduced) top of the atmosphere absorbed solar radiation and outgoing long wave radiation, as well as more (less) surface absorbed solar radiation, and surface sensible heat flux. During the positive IOD phase, enhanced convective activity and precipitation rainfall over the western Equatorial Indian Ocean, Arabian Sea, and west coast of India, with reduced rainfall over the eastern equatorial Indian ocean is accompanied with enhanced shortwave and long wave radiative fluxes over most parts of the Indian subcontinent. The conditions during the negative IOD phases are largely the opposite.

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975–2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0–20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate.

Satellite retrievals show a dipole-like pattern in composites of summer monsoon rainfall anomalies between the Indian region and the equatorial Indian Ocean (EIO) during the active (RMM phases 3, 4, 5, and 6) and suppressed phases (7, 8, 1, and 2) of the Madden Julian Oscillation (MJO). The north-eastern part of India shows an out-of-phase relationship with rest of the Indian land during different MJO phases. Moisture convergence anomalies largely explain the rainfall anomalies seen during the various MJO phases. Cyclonic wind anomalies are seen over eastern Arabian sea and the Bay of Bengal during active MJO phases. Positive (negative) rainfall anomalies are associated with positive (negative) CAPE anomalies over most parts of the Indian land, whereas there is an inverse relationship over the east coast of India. Timings of diurnal rainfall peaks are fairly robust across various MJO phases; however, the amplitudes vary significantly depending on the MJO phase and location. Some of the previously reported diurnal features, such as the propagation of convective systems over the Bay of Bengal from the west coast into the central and south Bay, are fairly robust across MJO phases. Convective systems forming over Sumatra and propagating into the eastern EIO are prominent during the suppressed and weak MJO periods, but not during the active period.