Future Climate Change Projections over the Indian Region
J. Sanjay, R. Krishnan, M. V. S. Ramarao, R. Mahesh, Bhupendra Bahadur Singh, Jayashri Patel, Sandip Ingle, Preethi Bhaskar, J. V. Revadekar, T. P. Sabin, M. Mujumdar
Assessments of impacts of climate change and future projections over the Indian region, have so far relied on a single regional climate model (RCM) - eg., the PRECIS RCM of the Hadley Centre, UK. While these assessments have provided inputs to various reports (e.g., INCCA 2010; NATCOMM2 2012), it is important to have an ensemble of climate projections drawn from multiple RCMs due to large uncertainties in regional-scale climate projections. Ensembles of multi-RCM projections driven under different perceivable socio-economic scenarios are required to capture the probable path of growth, and provide the behavior of future climate and impacts on various biophysical systems and economic sectors dependent on such systems.
The Centre for Climate Change Research, Indian Institute of Tropical Meteorology (CCCR-IITM) has generated an ensemble of high resolution downscaled projections of regional climate and monsoon over South Asia until 2100 for the Intergovernmental Panel for Climate Change (IPCC)using a RCM (ICTP-RegCM4) at 50 km horizontal resolution, by driving the regional model with lateral and lower boundary conditions from multiple global atmosphere-ocean coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The future projections are based on three Representation Concentration Pathway (RCP) scenarios (viz., RCP2.6, RCP4.5, RCP8.5) of the IPCC.
In contrast to the wet gets wetter and dry gets drier paradigm, here, using observations and climate model simulations, we show that the mean rainfall over the semi-arid northwest parts of India and Pakistan has increased by 10 to 50 percent during 1901 to 2015 and is expected to increase by 50 to 200 percent under moderate greenhouse gas (GHG) scenarios, e.g, SSP2 4.5. The GHG forcing primarily drives the westward expansion of the Indian summer monsoon (ISM) rainfall and is a result of a westward expansion of the inter-tropical convergence zone (ITCZ), facilitated by a westward expansion of t…
The Indian summer monsoon rainfall (ISMR) has a decisive influence on India's agricultural output and economy. Extreme deviations from the normal seasonal amount of rainfall can cause severe droughts or floods, affecting Indian food production and security. Despite the development of sophisticated statistical and dynamical climate models, a long-term and reliable prediction of the ISMR has remained a challenging problem. Towards achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near surface air temperature field. We uncover that some…
We use Indian temperature data of more than 100 years to study the influence of solar activity on climate. We study the Sun-climate relationship by averaging solar and climate data at various time scales; decadal, solar activity and solar magnetic cycles. We also consider the minimum and maximum values of sunspot number (SSN) during each solar cycle. This parameter SSN is correlated better with Indian temperature when these data are averaged over solar magnetic polarity epochs (SSN maximum to maximum). Our results indicate that the solar variability may still be contributing to ongoing climate…
In this paper an approach based on expectation maximization (EM) clustering to find the climate regions and a support vector machine to build a predictive model for each of these regions is proposed. To minimize the biases in the estimations a ten cross fold validation is adopted both for obtaining clusters and building the predictive models. The EM clustering could identify all the zones as per the Koppen classification over Indian region. The proposed strategy when employed for predicting temperature has resulted in an RMSE of $1.19$ in the Montane climate region and $0.89$ in the Humid Sub …
Tropical cyclones remain a major threat to the lives, property and economy of communities around the South West Indian ocean (SWIO), notably Southern Africa and Madagascar. This study uses the weather research forecast (WRF) model to perform a series of simulations for tropical cyclone Enawo with the aim of investigating the effect of an increase or decrease (by 2$^{\circ}C$) in sea surface temperature (SST) on the intensity of the tropical cyclone (using windspeed, precipitation and pressure as measures of cyclone intensity). The experiment uses the data from European Centre for Medium-Range …
Hydro-meteorological variables, like precipitation, streamflow are significantly influenced by various climatic factors and large-scale atmospheric circulation patterns. Efficient water resources management requires an understanding of the effects of climate indices on the accurate predictability of precipitation. This study aims at understanding the standalone teleconnection between precipitation across India and the four climate indices, namely, Niño 3.4, PDO, SOI, and IOD using partial wavelet analysis. The analysis considers the cross correlation between the climate indices while estimatin…
Recently it was demonstrated how climate data can be utilized to estimate regional wind power densities. In particular it was shown that the quality of the global scale estimate compared well with regional high resolution studies and a link between surface temperature and moist density in the estimate was presented. In the present paper the methodology is tested further, to ensure that the results using one climate data set are reliable. This is achieved by extending the study to include four ensemble members. With the confidence that one instantiation is sufficient a climate change data set, …
The quantitative prediction of the intensity of rainfall events (light or heavy) has remained a challenge in Numerical Weather Prediction (NWP) models. For the first time the mean coefficient of diffusional growth rates are calculated using an Eulerian-Lagrangian particle-based small-scale model on in situ airborne measurement data of Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) during monsoon over Indian sub-continent. The results show that diffusional growth rates varies in the range of 0.00025 - 0.0015(cm/s). The generic problem of the overestimation of light…
The Centre for Climate Change Research (CCCR;http://cccr.tropmet.res.in) at the Indian Institute of Tropical Meteorology (IITM; http://www.tropmet.res.in), Pune, launched in 2009 with the support of the Ministry of Earth Sciences (MoES), Government of India, focuses on the development of new climate modelling capabilities in India and South Asia to address issues concerning the science of climate change. CCCR-IITM has the mandate of developing an Earth System Model and to make the regional climate projections. An important achievement was made by developing an Earth System Model at IITM, which…
This study assesses a Continuous Data Assimilation (CDA) dynamical-downscaling algorithm for enhancing the simulation of the Indian summer monsoon (ISM) system. CDA is a mathematically rigorous technique that has been recently introduced to constrain the large-scale features of high-resolution atmospheric models with coarse spatial scale data. It is similar to spectral nudging but does not require any spectral decomposition for scales separation. This is expected to be particularly relevant for ISM, which involves various interactions between large-scale circulations and regional physical proc…
Despite major advances in climate science over the last 30 years, persistent uncertainties in projections of future climate change remain. Climate projections are produced with increasingly complex models which attempt to represent key processes in the Earth system, including atmospheric and oceanic circulations, convection, clouds, snow, sea-ice, vegetation and interactions with the carbon cycle. Uncertainties in the representation of these processes feed through into a range of projections from the many state-of-the-art climate models now being developed and used worldwide. For example, desp…
We examine implications of anthropogenic low pressure regions, - created by injecting heat from nuclear reactors, into atmosphere. We suggest the possibility that such artificially generated low pressure regions, near hurricanes could disrupt their growth, path, and intensity. This method can also create controlled tropical stroms, which lead to substantial rainfall in arid areas, such as - (1)Sahara desert, (2) Australian interior desert, and (3) Indian Thar desert. A simple vortex suction model is developed to study, effect on atmospheric dynamics, by such a nuclear heat injection system.
Climate change is predicted to lead to major changes in terrestrial ecosystems. However, significant differences in climate model projections for given scenarios of greenhouse gas emissions, continue to hinder detailed assessment. Here we show, using a traditional Koppen-Geiger bioclimate classification system, that the latest CMIP6 Earth System Models actually agree very well on the fraction of the global land-surface that will undergo a significant change per degree of global warming. Data from historical and ssp585 model runs are used to create bioclimate maps at various degrees of global w…
If climate stationarity is dead, how should engineering design standards be modified to account for potential changes in extreme precipitation? Many standards rely on precipitation intensity-duration-frequency (IDF) curves provided in NOAA's Atlas 14. General Circulation Models (GCMs) predict increases in average temperature throughout the US, but are less clear on changes in precipitation. In many areas GCMs predict relatively small changes in total precipitation volume, but also suggest increased magnitude of extreme events as warmer air can hold more water. Unfortunately, GCMs have limited …
Quantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce $likely$ (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpret…
Numerical climate models are used to project future climate change due to both anthropogenic and natural causes. Differences between projections from different climate models are a major source of uncertainty about future climate. Emergent relationships shared by multiple climate models have the potential to constrain our uncertainty when combined with historical observations. We combine projections from 13 climate models with observational data to quantify the impact of emergent relationships on projections of future warming in the Arctic at the end of the 21st century. We propose a hierarchi…
Assessing the consistency between short-term global temperature trends in observations and climate model projections is a challenging problem. While climate models capture many processes governing short-term climate fluctuations, they are not expected to simulate the specific timing of these somewhat random phenomena - the occurrence of which may impact the realized trend. Therefore, to assess model performance, we develop distributions of projected temperature trends from a collection of climate models running the IPCC A1B emissions scenario. We evaluate where observed trends of length 5 to 1…
Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951-2014) and projected (2015-2100) climate for the four scenarios (SSP126, SSP245, SS…
Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty in the projected frequencies of daily maximum temperature and precipitation extremes, which are defined as events that exceed the 99.97th percentile. This is done globally using initial-condition large ensembles. For maximum temperature extremes, internal variability dominates in the next two decades. Around the middle of the 21st century model and scenario uncertainty become the dominant contribution in the tropics b…
Multi-model projections in climate studies are performed to quantify uncertainty and improve reliability in climate projections. The challenging issue is that there is no unique way to obtain performance metrics, nor is there any consensus about which method would be the best method of combining models. The goal of this study was to investigate whether combining climate model projections by artificial neural network (ANN) approach could improve climate projections and therefore reduce the range of uncertainty. The equally-weighted model averaging (the mean model) and single climate model proje…
When the climate system is forced, e.g. by emission of greenhouse gases, it responds on multiple time scales. As temperatures rise, feedback processes might intensify or weaken. Current methods to analyze feedback strength, however, do not take such state dependency into account; they only consider changes in (global mean) temperature and assume all feedbacks are linearly related to that. This makes (transient) changes in feedback strengths almost intangible and generally leads to underestimation of future warming. Here, we present a multivariate (and spatially explicit) framework that facilit…
Sites for next-generation telescopes are chosen decades before the first light of a telescope. Site selection is usually based on recent measurements over a period that is too short to account for long-term changes in observing conditions such as those arising from anthropogenic climate change. In this study, we analyse trends in astronomical observing conditions for eight sites. Most sites either already host telescopes that provide in situ measurements of weather parameters or are candidates for hosting next-generation telescopes. For a fine representation of orography, we use the highest re…
Climate predictions are only meaningful if the associated uncertainty is reliably estimated. A standard practice for providing climate projections is to use an ensemble of projections. The ensemble mean serves as the projection while the ensemble spread is used to estimate the associated uncertainty. The main drawbacks of this approach are the fact that there is no guarantee that the ensemble projections adequately sample the possible future climate conditions and that the quantification of the ensemble spread relies on assumptions that are not always justified. The relation between the true u…
We study the relationship between the El Niño--Southern Oscillation (ENSO) and the Indian summer monsoon in ensemble simulations from state-of-the-art climate models, the Max Planck Institute Earth System Model (MPI-ESM) and the Community Earth System Model (CESM). We consider two simple variables: the Tahiti--Darwin sea-level pressure difference and the Northern Indian precipitation. We utilize ensembles converged to the system's snapshot attractor for analyzing possible changes (i) in the teleconnection between the fluctuations of the two variables, and (ii) in their climatic means. (i) With…
Future changes in the location of the intertropical convergence zone (ITCZ) due to climate change are of high interest since they could substantially alter precipitation patterns in the tropics and subtropics. Although models predict a future narrowing of the ITCZ during the 21st century in response to climate warming, uncertainties remain large regarding its future position, with most past work focusing on the zonal-mean ITCZ shifts. Here we use projections from 27 state-of-the-art climate models (CMIP6) to investigate future changes in ITCZ location as a function of longitude and season, in …
A high-resolution wave climate projection for the northwestern Atlantic Ocean has been conducted to help assess possible regional impacts due to global climate change. The spectral wave model NOAA WAVEWATCH III is utilized with three coupled (two-way) grids to resolve the northwestern Atlantic and coastal southern and eastern USA at approximately 21 km and 7 km respectively, and covers the periods 1979--2003 (historic) and 2075--2099 (future). Hourly wind field forcings are provided by a high-resolution AGCM (MRI-AGCM 3.2S; 21 km) and allow for better modeling of large storm events (important …
We have done a statistical analysis of the wind field from the archive of NCEP/NOAA over the Indian Ocean for the period 1998-2008yy, which is given on a grid 1x1.25 of latitude-longitude with 3h time-step. Initial analysis includes mapping the average wind fields <W(x,y,T> and fields of mean density of the wind-kinetic-energy flux <Ea(x,y,T>, obtained with different periods of time averaging T, as well as the assessment of 11-year trends in these fields. The subsequent analysis is concerned with partition of the Indian ocean area into 6 zones, provided by the spatial inhomogeneity of the anal…
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of California, Davis（加州大学戴维斯分校）
Almonds are one of the most lucrative products of California, but are also among the most sensitive to climate change. In order to better understand the relationship between climatic factors and almond yield, an automated machine learning framework is used to build a collection of machine learning models. The prediction skill is assessed using historical records. Future projections are derived using 17 downscaled climate outputs. The ensemble mean projection displays almond yield changes under two different climate scenarios, along with two technology development scenarios, where the role of t…
It is shown in this comment that considering the Mauna Loa observation of the atmospheric carbon dioxide concentration and the mean near surface temperature anomalies for the period from the beginning of the seventies to recent years only is, clearly, a source of misinterpretation. If we consider the whole period of available data (1958 - 2004), we obtain results which differ from those presented by Rahmstorf et al. It is also shown that in 1988 when the Intergovernmental Panel of Climate Change (IPCC) of the United Nations and the World Meteorological Organization (WMO) was established there …
Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider …
Global warming arises from 'temperature forcing', a net imbalance between energy fluxes entering and leaving the climate system and arising within it. Humanity introduces temperature forcing through greenhouse gas emissions, agriculture, and thermal emissions from fuel burning. Up to now climate projections, neglecting thermal emissions, typically foresee maximum forcing around the year 2050, followed by a decline. In this paper we show that, if humanity's energy use grows at 1%/year, slower than in recent history, and if thermal emissions are not controlled through novel energy technology, te…
Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes
National Institute of Water and Atmospheric Research (NIWA)（国家水气和大气研究所）；ARC Centre of Excellence for Weather of the 21st Century（21世纪天气卓越研究中心）；University of New South Wales（新南威尔士大学）；Climate Change Research Centre, University of New South Wales（新南威尔士大学气候变化研究中心）；Te Aka Mātuatua School of Science, University of Waikato（怀卡托大学理学院）
Generative artificial intelligence improves projections of climate extremes
Artificial Intelligence Innovation and Incubation Institute（人工智能创新与孵化院）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Institute for Big Data（大数据研究院）；MOE Laboratory for National Development and Intelligent Governance（教育部国家发展与智能治理实验室）；Shanghai Academy of AI for Science（上海人工智能科学研究院）；FuXi Intelligent Computing Technology Co., Ltd.（复旦智能计算技术有限公司）
Climate models are thought to solve boundary value problems unlike numerical weather prediction, which is an initial value problem. However, climate internal variability (CIV) is thought to be relatively important at near-term (0-30 year) prediction horizons, especially at higher resolutions. The recent availability of significant numbers of multi-model (MME) and multi-initial condition (MICE) ensembles allows for the first time a direct sensitivity analysis of CIV versus model response variability (MRV). Understanding the relative agreement and variability of MME and MICE ensembles for multip…
Global Climate Model Bias Correction Using Deep Learning
Department of Computational；Data Sciences, Indian Institute of Science, Bengaluru 560012, India. Divecha Center for Climate Change, Indian Institute of Science, Bengaluru 560012, India.
We review the skill of thirty coupled climate models participating in Coupled Model Intercomparison Project 5 in terms of reproducing properties of the seasonal cycle of precipitation over the major river basins of South and Southeast Asia (Indus, Ganges, Brahmaputra and Mekong) for historical period (1961-2000). We also present projected changes by these models by end of century (2061-2100) under extreme scenario RCP8.5. First, we assess their ability to reproduce observed timings of the monsoon onset and the rate of rapid fractional accumulation (RFA slope) - a measure of seasonality within …
While previous model sensitivity studies have mainly focused on discerning the soil moisture-precipitation feedback processes over the Indian region, the present study investigates the impact of soil moisture-temperature (SM-T) coupling on the temperature extremes (ExT) using the high-resolution (~60 km) model simulations. These simulations include the control and soil moisture (SM) sensitivity experiments (DRY-SM and WET-SM) initialized by perturbing (decreasing/increasing) SM from the historical (HIST: 1951-2010) and future 4K warming (FUT: 2051-2100) control runs. The analysis identifies th…
Massive river interlinking projects are proposed to offset observed increasing trends of extremes, such as droughts and floods in India, the second highest populated country.These river interlinking projects involve water transfer from surplus to deficit river basins through reservoirs and canals, but without an in-depth understanding of the hydro-meteorological consequences. Using information theory-based causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, we show that causal pathways exist across different basins in India due to strong land-atmo…
Assessing and managing risks in a changing climate requires projections that account for decision-relevant uncertainties. These deep uncertainties are often approximated by ensembles of Earth-system model runs that sample only a subset of the known uncertainties. Here we demonstrate and quantify how this approach can cut off the tails of the distributions of projected climate variables such as sea-level rise. As a result, low-probability high-impact events that may drive risks can be under-represented. Neglecting the tails of this deep uncertainty may lead to overconfident projections and poor…
